Arts, Faculty of
Anthropology, Department of
DSpace
UBCV
Sacharuk, Jasmine
Doctor of Philosophy - PhD
University of British Columbia
This dissertation explores the connection between identity transformation and ceramic technology in the Erligang Period (~1600-1300 BCE), focusing on differences in microstructure between the sites of Zhengzhou in Henan and Panlongcheng in Hubei, China. Using digital image analysis to determine grain- and void-size morphology and firing temperature, I explore how Panlongcheng settlers changed their ceramic practices and developed new formal and technological styles despite maintaining some typological similarities to the Central Plains. A methodology for extracting particle and firing temperature data from SEM-BSE images is developed and presented. This provides quantitative and qualitative comparisons in raw material acquisition, processing and firing temperature, and consideration of changes in the production process and functional aspects of ceramics at Panlongcheng. This dissertation also analyzes a rare Panlongcheng bronze-casting mold sample found in the interior of a bronze handle and presents evidence that mold-making may have also differed at Panlongcheng. I argue that Panlongcheng ceramic microstructure, in conjunction with additional evidence from recent excavations at the site, indicate a population reminiscent of a diaspora who maintain components of a homeland while adapting to the environmental and social contexts of their new settlement. I also contend that the analysis of ceramic technology is a crucial dimension in understanding the internal dynamics of sites across the landscape during the Erligang Period, whose identities – reflected in their ways of doing – were likely also shaped by ongoing and embodied connections with the Central Plains while adapting to new social and environmental contexts in unique and localized ways.
Cultural Transformation and Diaspora during the Erligang Period: A Case Study via Digital Image Analysis of Panlongcheng Ceramic Microstructure by JASMINE SACHARUK B.A., University of Alberta, 2008 B.A., University of British Columbia, 2012 M.A., University of British Columbia, 2014 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Anthropology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) January 2025 © Jasmine Sacharuk, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Cultural Transformation and Diaspora during the Erligang Period: A Case Study via Digital Image Analysis of Panlongcheng Ceramic Microstructure submitted by Jasmine Sacharuk in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Anthropology Examining Committee: Dr. Zhichun Jing, Associate Professor, Anthropology, UBC Supervisor Dr. Lisa Cooper, Professor, Classical, Near Eastern and Religious Studies, UBC Supervisory Committee Member Dr. Andrew Martindale, Professor, Anthropology, UBC University Examiner Dr. Kevin Fisher, Associate Professor, Ancient Mediterranean and Near Eastern Studies, UBC University Examiner Dr. Anne Underhill, Professor, Anthropology, Yale University External Examiner Additional Supervisory Committee Members: Dr. David Pokotylo, Associate Professor Emeritus, Anthropology, UBC Supervisory Committee Member iii Abstract This dissertation explores the connection between identity transformation and ceramic technology in the Erligang Period (~1600-1300 BCE), focusing on differences in microstructure between the sites of Zhengzhou in Henan and Panlongcheng in Hubei, China. Using digital image analysis to determine grain- and void-size morphology and firing temperature, I explore how Panlongcheng settlers changed their ceramic practices and developed new formal and technological styles despite maintaining some typological similarities to the Central Plains. A methodology for extracting particle and firing temperature data from SEM-BSE images is developed and presented. This provides quantitative and qualitative comparisons in raw material acquisition, processing and firing temperature, and consideration of changes in the production process and functional aspects of ceramics at Panlongcheng. This dissertation also analyzes a rare Panlongcheng bronze-casting mold sample found in the interior of a bronze handle and presents evidence that mold-making may have also differed at Panlongcheng. I argue that Panlongcheng ceramic microstructure, in conjunction with additional evidence from recent excavations at the site, indicate a population reminiscent of a diaspora who maintain components of a homeland while adapting to the environmental and social contexts of their new settlement. I also contend that the analysis of ceramic technology is a crucial dimension in understanding the internal dynamics of sites across the landscape during the Erligang Period, whose identities – reflected in their ways of doing iv – were likely also shaped by ongoing and embodied connections with the Central Plains while adapting to new social and environmental contexts in unique and localized ways. v Lay Summary I explore the connection between identity and ceramic technology in the Erligang Period in China (~1600-1300 BCE) by comparing ceramics from two sites of this period - Zhengzhou and Panlongcheng. Based on earlier research, Panlongcheng residents had relocated from Zhengzhou. I use digital image analysis to extract data from microscope images of ceramic materials. This information helps us understand how the ceramics were made and what they were made of, which can help us compare differences in manufacturing materials and techniques between sites. The way people make things can be informative about culture and identity. I argue that Panlongcheng ceramic technology indicates a population who maintained connections to their homeland while evolving other facets due to their new environmental and social context, similar to diasporic populations. I also argue that analyzing ceramic technology is critical for understanding the local and cultural dynamics of sites during this period. vi Preface This dissertation reflects original thought, research, and work by the author, Jasmine Sacharuk. Generative AI was not used for any aspect of this work. Electron microscopy imaging was provided by Dr. Zhichun Jing for Chapters 5 through 7. I formulated and designed the research concept, wrote all areas of the manuscript, developed the analytical methodology, and executed all statistical analyses. vii Table of Contents Abstract ...................................................................................................................... iii Lay Summary ................................................................................................................ v Preface ....................................................................................................................... vi Table of Contents ........................................................................................................ vii List of Tables ................................................................................................................ x List of Figures .............................................................................................................. xi Acknowledgements .................................................................................................. xviii Chapter 1: Introduction ................................................................................................. 1 Chapter 2: Diaspora: An Identity of Dual Consciousness ................................................. 7 2.1 Diaspora, Identity, and Archaeological Material .................................................... 16 Chapter 3: Panlongcheng Research History and Material Culture ................................... 26 3.1 Panlongcheng .................................................................................................... 34 3.1.1 Geographical and Chronological Context ....................................................... 34 3.1.2 Geomorphology and Area Divisions ............................................................... 36 3.2.3 Connecting Panlongcheng to the Central Plains ............................................. 39 3.2 Recent Excavations and Analyses ........................................................................ 41 3.2.1 Bronzes ....................................................................................................... 41 3.2.2 Metallurgy .................................................................................................... 45 3.2.3 Casting ........................................................................................................ 47 3.2.4 Molds .......................................................................................................... 49 3.2.5 Ceramics ..................................................................................................... 51 3.2.6 Soils ............................................................................................................ 59 3.3 Conclusion ........................................................................................................ 66 Chapter 4: Digital Image Analysis: Principles and Applications ....................................... 69 4.1 Digital Image Analysis in Archaeology .................................................................. 71 4.2 Digital Image Analysis versus Human Vision ......................................................... 75 4.3 Digital Image Acquisition..................................................................................... 78 4.4 Digital Image Composition .................................................................................. 79 viii 4.5 Digital Image Analysis Process ............................................................................ 82 4.5.1 Image Acquisition ......................................................................................... 82 4.5.2 Open-Source Image Analysis Software .......................................................... 84 4.5.3 Image Segmentation ..................................................................................... 85 4.5.4 Image Processing and Artefact Removal ........................................................ 87 4.5.5 Data Processing ........................................................................................... 88 4.6 Digital Image Analysis and Thin-Section Petrography ............................................ 89 Chapter 5: Panlongcheng and Zhengzhou Grain-Size Distribution and Morphology .......... 94 5.1 Grain Size and Morphology .................................................................................. 94 5.2 Digital Image Analysis of Panlongcheng and Zhengzhou Samples .......................... 96 5.2.1 Methodology ................................................................................................ 99 5.2.2 Image Pre-processing ................................................................................. 103 5.2.3 Training and Segmentation .......................................................................... 103 5.2.4 Results - Grains .......................................................................................... 105 5.2.5 Voids ............................................................................................................ 132 5.3 Discussion ....................................................................................................... 137 5.4 Conclusion ...................................................................................................... 139 Chapter 6: Exploring Differences in Firing Technology via SEM and Digital Image Analysis ................................................................................................................................ 145 6.1 Changes During Firing ....................................................................................... 147 6.2 Firing Technology in Archaeological Research .................................................... 151 6.3 Methodology .................................................................................................... 158 6.4 Training and Segmentation ................................................................................ 160 6.4.1 Results ...................................................................................................... 161 6.4.2 Voids ......................................................................................................... 165 6.5 Discussion ....................................................................................................... 177 6.6 Conclusion ...................................................................................................... 182 Chapter 7. Panlongcheng Bronze Casting Ceramic Technology .................................... 186 7.1 Analysis ........................................................................................................... 190 7.1.1 Methodology .............................................................................................. 190 ix 7.1.2 Training and Segmentation .......................................................................... 191 7.1.3 Results - Grains .......................................................................................... 194 7.1.4 Results - Voids ........................................................................................... 206 7.2 Discussion ....................................................................................................... 210 7.3 Conclusion ...................................................................................................... 213 Chapter 8. Conclusion .............................................................................................. 215 8.2 Summary and Overview of Contributions ........................................................... 217 8.3 Future Directions.............................................................................................. 221 References ............................................................................................................... 226 Appendices .............................................................................................................. 243 Appendix A. Supplemental Material for Chapter 5 .................................................... 243 A.1 SEM-BSE Images of Untempered Samples – Zhengzhou .................................. 243 A.2 SEM-BSE Images of Untempered Samples – Panlongcheng.............................. 247 A.3 SEM-BSE Images of Gang Samples – Zhengzhou ............................................. 254 A.4 SEM-BSE Images of Gang Samples – Panlongcheng ........................................ 258 A.5 SEM-BSE Images of Li Samples – Zhengzhou ................................................... 264 A.6 SEM-BSE Images of Li Samples - Panlongcheng .............................................. 267 Appendix B. Supplemental Material for Chapter 6 .................................................... 273 B.1 SEM-BSE Images of Untempered Samples – Zhengzhou .................................. 273 B.2 SEM-BSE Images of Untempered Samples – Panlongcheng ............................. 276 B.3 SEM-BSE Images of Gang Samples – Zhengzhou ............................................. 278 B.4 SEM-BSE Images of Gang Samples – Panlongcheng ........................................ 280 B.5 SEM-BSE Images of Li Samples – Zhengzhou ................................................... 283 B.6 SEM-BSE Images of Li Samples – Panlongcheng .............................................. 286 B.7 SEM-BSE Images of Zun/Protoporcelain - Panlongcheng.................................. 289 Appendix C. Supplemental Material for Chapter 7 .................................................... 292 C.1 SEM-BSE Images of Molds – Yinxu .................................................................. 292 C.2 SEM-BSE Images of Molds - Panlongcheng ..................................................... 295 x List of Tables Table 1. Number of Samples and Image Analyzed. ...................................................... 101 Table 2. Counts of Sand Grains Analyzed. ................................................................... 122 Table 3. Statistical Moments - Feret Diameter (μm) of Silt Grains (15-63 μm). ................ 124 Table 4. Counts of Voids (>50μm2) Analyzed................................................................ 134 Table 5. Statistical Moments - Void Geodesic Diameter (μm). ...................................... 137 Table 6. Number of Images per Site and Type (Firing Analysis). ..................................... 159 Table 7. Confusion Matrix of Random Forest Test Predictions. ..................................... 174 Table 8. Random forest model - void morphology prediction results. The model correctly predicts each site based on morphological characteristics of the voids for all except UBC06-42, the stoneware zun with the highest quantity of micropores. ....................... 177 Table 9. Number of Mold Samples and Images Analyzed. ............................................. 190 Table 10. Statistical Moments - Feret Diameter (μm) of Grains (15-125 μm) in Mold Samples. .................................................................................................................. 200 Table 11. Statistical Moments - Feret Diameter (μm) of Silt Grains (15-63 μm) in All Samples. .................................................................................................................. 201 Table 12. Number of Voids Analyzed in Mold Samples. ................................................ 207 Table 13. Summary of Results (Chapters 5 & 6) ........................................................... 219 xi List of Figures Figure 1. Panlongcheng site areas. ............................................................................... 26 Figure 2. Erligang distribution map – select sites. Created by Scott Humphrey. ............... 27 Figure 3. Li – a) Panlongcheng vs. b) Yinxu. .................................................................... 52 Figure 4. Hubei & Henan 1:1 million soil type map (2018). Anyang (Yinxu), Zhengzhou and Wuhan (Panlongcheng) are outlined in black................................................................. 62 Figure 5. Bisection illusion. The lines are the same length despite the vertical line appearing longer. ........................................................................................................ 75 Figure 6. Müller-Lyer Illusion. The lines in the center are identical but appear different due to the direction of the arrowheads. ............................................................................... 76 Figure 7. Kanizsa Triangle. Humans are able to fill in gaps and see shapes or patterns despite missing or occluded visual information. ............................................................ 77 Figure 8. Image segment (left) and underlying pixel values (right). ................................... 80 Figure 9. Low-resolution vs. high resolution. ................................................................. 81 Figure 10. Otsu’s Thresholding. .................................................................................... 86 Figure 11. WEKA trainable segmentation overlay, where the colors of the labels are overlaid atop the original image. This helps the user visually assess the accuracy of the model (and continue to improve the classification with additional data, if necessary). ...................... 99 Figure 12. Untempered ceramic assemblage a) guan b) pen c) zeng d) zun e) proto-porcelain lid (Wang et al. 2016) .................................................................................. 102 Figure 13. Gang with various bases. (Wang et al. 2016) ................................................ 102 Figure 14. Closing a cracked particle. ......................................................................... 104 xii Figure 15. Guan SEM images and isolations. a) Zhengzhou guan sample b) Panlongcheng guan sample c) Zhengzhou guan sample - isolated particles d) Panlongcheng guan sample - isolated particles. ................................................................................................... 107 Figure 16. Cumulative frequency of untempered samples............................................ 107 Figure 17. Cumulative Frequency of Untempered Samples by Site. .............................. 108 Figure 18. Gang SEM images and isolations. a) Zhengzhou gang b) Panlongcheng gang c) Zhengzhou gang – isolated particles d) Panlongcheng gang – isolated particles. ............ 109 Figure 19. Cumulative frequency of gang samples. ...................................................... 110 Figure 20. Li SEM images and isolations. a) Zhengzhou li b) Panlongcheng li c) Zhengzhou li - isolated particles d) Panlongcheng li - isolated particles. Zhengzhou li are clay-heavy, resulting in few segmented grains. Panlongcheng li are still dominated by small silt particles. .................................................................................................................. 111 Figure 21. Cumulative frequency of li samples. Panlongcheng li samples cluster together with one exception. Zhengzhou li samples all have higher frequencies of larger silt particles than Panlongcheng li. ................................................................................................ 112 Figure 22. Cumulative frequency of grain size by site and type. Zhengzhou li and untempered are similar but the Zhengzhou gang is closer in frequency to Panlongcheng types. ....................................................................................................................... 112 Figure 23. Total area percentage of each grain class per image - untempered. Most Zhengzhou samples have large proportions of coarse silt. ........................................... 113 xiii Figure 24. Average image area percentage of each grain class by site – untempered. Zhengzhou samples have more silt overall, and 3.5 times more coarse silt in terms of the area of the image. ..................................................................................................... 114 Figure 25. Average image area percentage of grain size per sample - untempered. Zhengzhou samples have larger quantities of grains above 50 μm while Panlongcheng samples are more positively skewed. ......................................................................... 115 Figure 26. Total area percentage of each grain class per image - gang. Gang samples from Zhengzhou have similar quantities and proportions of silt to Panlongcheng samples unlike other ceramic types. ................................................................................................. 116 Figure 27. Average image area percentage of each grain class by site - gang. This time, Panlongcheng samples have slightly higher quantities of coarse silt but both are in line with the average composition seen in Panlongcheng untempered samples. This suggests Panlongcheng origin of gang found in Zhengzhou. ....................................................... 117 Figure 28. Average image area percentage of grain size per sample - gang. All samples have positively-skewed distributions with small quantities of grains >50 μm. ........................ 118 Figure 29. Total area percentage of each grain class per image - li. Panlongcheng li have far higher quantities of silt compared to Zhengzhou li across all grain classes. ................... 118 Figure 30. Average image area percentage of each grain class by site - li. Silt takes up 2-3x more area in Panlongcheng li samples compared to Zhengzhou li samples. .................. 119 Figure 31. Average image area percentage of grain size per sample - li. The sparse quantity of silt in Zhengzhou li is evident while Panlongcheng li still have high quantities of medium silt. .......................................................................................................................... 119 xiv Figure 32. Average image area percentage of silt grain class by site and type. The amount of silt across all grain classes is similar for Panlongcheng types but differ at Zhengzhou. ... 120 Figure 33. Average image area percentage of sand grain class by site and type. The sample size is limited but suggests differences in sand across sites, with high quantities of fine sand for Zhengzhou li in particular. Note that these results are based on sand visible within the field of view. ........................................................................................................ 122 Figure 34. Predicted probabilities for untempered samples - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter. .......................................... 127 Figure 35. Predicted probabilities for untempered samples - site ~ feret + solidity + roundness + circularity. Grains are more likely to be from Zhengzhou if they are larger, rounder and less angular. .......................................................................................... 128 Figure 36. Predicted probabilities for Panlongcheng gang/Zhengzhou untempered samples - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter. ................................................................................................................................ 129 Figure 37. Predicted probabilities for Panlongcheng gang/Zhengzhou untempered samples - site ~ feret + solidity + roundness + circularity. Grains are again more likely to be from Zhengzhou if they are larger, rounder and less angular. ................................................ 130 Figure 38. Predicted probabilities for li - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter, but to a lesser degree than other types. More data are needed since Zhengzhou samples have so few grains. .................................... 131 Figure 39. Cumulative frequency of void length by site and type. .................................. 133 xv Figure 40. Average image area percentage of void length - untempered samples. Zhengzhou samples tend to have more voids than Panlongcheng samples. .................. 135 Figure 41. Average image area percentage of void length - li samples. Zhengzhou samples tend to have longer voids, likely due to clay shrinking away from large sand grains. ....... 136 Figure 42. Appearance of “shaggy” clay matrix in SEM-BSE. ......................................... 149 Figure 43. Appearance of vitrification in SEM-BSE. ....................................................... 150 Figure 44. Fine bloating pores. ................................................................................... 156 Figure 45. Coarse bloating pores. ............................................................................... 157 Figure 46. Changes in area percentages - temperature. Glass increases rapidly after ~850 degrees C. ................................................................................................................ 161 Figure 47. Vitrification – some particles still visible but integrating with the rest of the body. ................................................................................................................................ 162 Figure 48. Changes in area percentages - matrix/glass. When samples have a higher percentage of glass in the image, one can consider it “high-fired”; conversely, “low-fired” samples have a higher percentage of matrix. .............................................................. 163 Figure 49. Percentage of matrix versus glass by site and type. Zhengzhou samples are all high-fired; Panlongcheng samples are almost all low-fired except for stoneware zun and protoporcelain samples. ........................................................................................... 164 Figure 50. Cumulative frequency of void size by temperature. ...................................... 166 Figure 51. Cumulative frequency of voids highlighting temperatures above 800°C. Voids grow to their longest at 800°C-900°C but rapidly shrink as clay starts to vitrify. .............. 166 Figure 52. Number of micropores per temperature sample (500x magnification). .......... 168 xvi Figure 53. Number of micropores per site sample (100x magnification). Panlongcheng’s stoneware zun sample has by far the highest amount of micropores. Micropores develop as a function of temperature, atmosphere and composition (presence/absence of CaO). ................................................................................................................................ 169 Figure 54. Number of micropores per site sample - excluding UBC06-42 (100x magnification). Outside of Panlongcheng’s stoneware zun sample, Zhengzhou samples tend to have higher quantities of micropores. ............................................................. 170 Figure 55. Tortuosity calculation to measure the total path of an irregular line. .............. 172 Figure 56. Variable importance for random forest model. ............................................. 175 Figure 57. a) Panlongcheng mold b) Yinxu mold c) Panlongcheng mold isolated particles d) Yinxu mold isolated particles. .................................................................................... 193 Figure 58. Cumulative frequency of mold samples. Panlongcheng mold samples have higher quantities of larger grains compared to Zhengzhou mold samples. ..................... 194 Figure 59. Cumulative frequency of molds samples by site. ......................................... 195 Figure 60. Cumulative frequency of central plains (Zhengzhou/Yinxu) ceramics by type. The types are roughly similar in cumulative frequency except for gang, which is likely to have originated in Panlongcheng. ....................................................................................... 197 Figure 61. Cumulative frequency of Panlongcheng ceramics by type. The types are roughly similar except for the mold samples, suggesting a different area of production. ............ 198 Figure 62. Average image area percentage of each grain class by site for mold sample. The Panlongcheng mold samples are heavy in very fine sand and coarse silt; Yinxu (Xiaomintun)’s samples are coarse/medium-silt heavy like its other ceramic products. . 198 xvii Figure 63. Average image area percentage of grain size per sample (molds). ................. 199 Figure 64. Predicted probabilities for mold samples - Site ~ Feret. Grains are less likely to be from Yinxu/Xiaomintun as they grow in feret diameter. ............................................ 203 Figure 65. Predicted probabilities for mold samples - Site ~ Feret + Circularity. Grains are less likely to be from Yinxu/Xiaomintun as they grow in feret diameter and get less circular. ................................................................................................................................ 204 Figure 66. Predicted probabilities for Panlongcheng ceramics - Type ~ Feret. A grain is more likely to be from a mold as it grows in Feret diameter. .................................................. 205 Figure 67. Predicted probabilities for Panlongcheng ceramics - Type ~ Feret + Circularity + Solidity + Roundness + Aspect Ratio. A grain is more likely to be from a mold as it grows in Feret diameter, becomes more rounded, longer and less angular, but decreases in circularity. ................................................................................................................ 205 Figure 68. Cumulative frequency of void geodesic diameter for mold samples. ............. 208 Figure 69. Percentage of image – voids (mold samples). The differences observed are primarily due to larger grains in Panlongcheng samples taking up more area. ................ 208 Figure 70. Average image area percentage of each void geodesic diameter by mold sample. ................................................................................................................................ 209 xviii Acknowledgements This dissertation would have not been possible without the encouragement and guidance of my supervisor, Dr. Zhichun Jing, whose field school in Anyang changed the course of my academic career. I feel incredibly lucky to have gotten to work with Jing, whose love of archaeology is boundless and infectious, and who has continuously supported me through many peaks and valleys. I also need to extend my deep gratitude to my supervisory committee members, Dr. David Pokotylo and Dr. Lisa Cooper. I can say with all seriousness that I simply would not have been able to finish without their help, expertise, kindness, and about a million readthroughs of this dissertation. Thank you both so much. I would like to thank all of the scholars in China who helped facilitate this research and assisted in various aspects of sample acquisition: Dr. Niu Shishan, Dr. Yue Zhanwei and Dr. He Yulin (Institute of Archaeology at the Chinese Academy of Social Sciences), Dr. Tang Jigen (Southern University of Science and Technology), Dr. Zhang Changping (Wuhan University), Dr. Chen Hui (Hangzhou City University), Dr. Yang Shugang (Henan Institute of Cultural Heritage and Archaeology) and Dr. He Weidong (Henan University). Additionally, a sincere thanks to all of the staff at the workstations at both Anyang and Panlongcheng, who showed such warmth, hospitality and patience to this bumbling foreigner. This research was supported by generous funding through a Social Science and Humanities Research Council of Canada Joseph-Armand Bombardier CGS Doctoral Fellowship, a UBC Four Year Fellowship, a MITACS Globalink Research Award, a UBC xix Department of Anthropology PhD Dissertation Writing Award and UBC Faculty of Arts Graduate Awards. I’ve met so many amazing people over the course of this project. Thank you to Natasha, Steffan, Mengyang, Lauren, Emma, Dani, Christina and Yi-Ling – some of my most cherished memories are being in China with you all. Maybe one day we can all meet again at the beer garden by the Anyang River. I’m incredibly appreciative of my friends who have offered levity and managed to get me away from my screen once in a while, and my dog Tuna, who I got in the first year of this Ph.D. and is snoozing next to me as I write this. And I am so grateful for my wonderful and supportive partner Michael, who has kept me laughing and has patiently listened to me yammer on about this project for so very many years. Thank you and I love you. Finally, thank you to my dear and bizarre and delightful and brilliant family – Mom, Jan, and Vanessa. I’m so lucky to have you guys by my side. And to my dad, who passed away before I even became an archaeologist. He was a scientist and would have loved to hear about my archaeological adventures. This one’s for you, Dad. 1 Chapter 1: Introduction Panlongcheng, an early Bronze Age site (~1600-1250 BCE) at the north bank of the Yangtze River near the modern-day city of Wuhan in Hubei, China, is the southernmost major Erligang site and the largest source of Erligang material culture. Based on a sudden appearance of materials similar to those at a site, Zhengzhou, nearly 500 kilometers to the north, it is thought that settlers from Zhengzhou and related sites migrated to Panlongcheng (Bagley 1977; Zhang 2014). Since its discovery in 1954, much has been written about, and speculated upon, regarding Panlongcheng’s relationship with Zhengzhou, the largest Erligang site and presumed capital of the early Shang dynasty (also ~1600-1250 BCE) (cf. Bagley 1977, 2014; Campbell 2022; Liu and Chen 2012; Wang 2014). Much of the literature on Panlongcheng is framed in terms of its role as subservient to Zhengzhou, with the longstanding theory being that it was a supplier of raw metallurgical materials for Zhengzhou’s massive bronze industry (cf. Bagley 1977; Liu & Chen 2001; Wang 2014). The framing in both English and Chinese literature is through what is essentially the core-periphery model, whether explicitly or implicitly, where Zhengzhou is the central power and locus of wealth and its “subordinates” are just smaller, if not lesser, forms of this center. As Li (2014) notes, the way in which Bronze Age China archaeology has been understood has largely been carried over via a dynastic framework that resembles the core-periphery model, inheriting the perspectives of traditional history that focus on territorial control and assertions of power emanating from a dynastic capital. The history of excavation has also been a major driver of the persistence of this framework, as the discovery of Yinxu and the wealth of finds there (including extant writing) has placed 2 Yinxu at the center of most Bronze Age China literature. The structure of Shang literature often reifies this framework by discussing the larger site first and in great detail, where other sites are covered briefly in later sections. As the site is located underneath the modern-day city of Zhengzhou, it has yielded far fewer finds, but its overall size and proximity to Yinxu has led to the assumption that it is simply an earlier form of the late Shang capital. As such, Erligang literature regularly follows this same structure. While there are exceptions, it is remarkably difficult to extract oneself from this model because of the comparative wealth of literature framed from this perspective. But such a perspective flattens what was most likely a dynamic, diverse, multidirectional system of interregional interaction (Campbell 2014a, 2014b; Keightley 1983; Li 2014; Jing 2012). Additionally, the focus is so heavy on the macro-scale processes connecting Panlongcheng, Zhengzhou and other sites that the internal dynamics of Panlongcheng are rarely addressed. This is despite bottom-up processes being a key part of the negotiation, contestation and/or maintenance of power, especially when it comes to long-distance networks, and also being an important way to understand the experiences of humans who made their homes and lives at these smaller settlements. In recent years, however, there have been attempts to refocus attention on Panlongcheng and its own internal dynamics and unique components of its material culture (cf. Liu 2019; Sun 2017; Zhang 2014). Much of the focus on macro-scale processes is influenced by the heavy focus on “indexical” markers of Erligang civilization, with research focusing heavily on bronze distribution and stylistic analyses (e.g., Bagley 1977; Liu and Chen 2012; Wang 2014). While bronzes are no doubt an important part of this period, the resulting frameworks 3 either implicitly (like Bagley 1977) or explicitly (like Wang 2014) underutilize other material sources (like ceramic technology) to build their models. From these perspectives we end up seeing an Erligang center spreading its symbols of power outward but would “still be no closer to understanding the actual mechanics of power or organization of relationships between these centers and the Zhengzhou metropole” (Campbell 2014b: 125). These perspectives are reinforced by examining the spread of ceramic types to different sites, but “without information concerning the production, distribution, and consumption of ceramics, the social and political significance of ceramic distribution is unclear” (Campbell 2014b: 125). To help refocus attention toward Panlongcheng and the migrants who settled there, I use the framework of diaspora via habitus, chaîne opératoire and technological style to understand processes of continuity and change visible in Erligang ceramic technology. Diaspora theory looks at the ways in which migrants, particularly those encountering unfamiliar groups, gradually develop a new identity in new spaces and situations (Cipolla 2017; Hall 1990; Gilroy 1993). These migrants maintain particular practices that harken back to the ‘homeland’ while developing new practices of their own, creating an identity defined by relative difference. When moving into new lands, non-human actors such as the environment - geology, flora, fauna - are active components of a community and play an agentive role in the development of this new identity (Cipolla 2017; Malouchos 2020). In understanding settlers across the Erligang landscape, including Panlongcheng, as diasporic, we can consider how the dynastic model of the “Shang state” and “Shang identity” is insufficient for conceptualizing these populations, and how this framework 4 inspires further questions and considerations regarding the dynamics of Bronze Age China at the macro-scale. I focus specifically on ceramic technology because of its connection with everyday practice and its relative flexibility in production (as compared to metallurgy, for example). Due to the regular production of ceramics in and for domestic spaces, and the ease of raw materials acquisition, ceramics can help reflect both the routinized acts of pottery production while also reflecting changes in the craft occurring due to various external influences. As Hegmon (1998: 277) argues, “Material (including domestic ceramics and architecture) that is used in and that structures everyday domestic life may be particularly relevant to the concept of habitus; that is, such material plays an important role in defining who people are socially”. This social connection embedded in ceramic materials and their production can therefore highlight the changes experienced by and developed by diasporic populations encountering new places, environments, and people. My aim is to establish baselines for comparison in ceramic technology between Panlongcheng and Zhengzhou to understand more about local practices and choices in ceramic production at Panlongcheng as settlers adapted to and experimented with their new environment. The questions I want to explore are: How does the analysis of ceramic microstructure reflect production activities in Panlongcheng, and what are the qualitative and quantitative similarities and differences compared to Zhengzhou? What do the results tell us about diaspora and identity creation at Erligang and what does this suggest about cultural development during the Erligang expansion? 5 I begin in Chapter 2 by exploring more fully the concept of diaspora and its relationships with other topics such as migration, colonialism, culture contact, ethnogenesis, identity and practice theory. While the concept of diaspora extends back millennia, its usage in archaeology, aside from some notable exceptions, has been relatively limited until recently. I also discuss the ways in which diaspora can be and has been analyzed via habitus, chaîne opératoire and the concept of technological style. Chapter 3 continues by contextualizing Panlongcheng material culture, its history and place in early Bronze Age China archaeology and explores more recent research regarding the local identity of Panlongcheng materials and practices. Chapter 4 explains the technology and methodology behind digital image analysis before moving onto a preliminary analysis of ceramic technology from Panlongcheng and Zhengzhou. To parse some characteristics of each sites’ technological style, this dissertation uses digital image analysis to extract quantitative information about particle and void size and morphology from scanning electron microscope backscattered electron images (SEM-BSE) of Erligang ceramics. The question of diaspora and other identity-related concepts are closely tied to practice, chaîne opératoire and technological style - focusing on how, as opposed to what, things are made (Dobres 1999; Lemmonier 1983; Leroi-Gourhan 1964). The extraction of quantitative information like grain- and void-size distribution and morphology helps provide insight into similarities and differences in technology like raw material selection, processing and firing temperature in ceramic production. 6 Chapter 5 focuses on baseline differences in grain- and void-size distribution across three categories of ceramics (li cooking vessels, gang vats, and various types of ceramics without visible inclusions – guan, pen, gui, zeng, da kou zun – grouped broadly as an “untempered” class) and focuses on differences in firing technology in Chapter 6.In Chapter 7, I examine differences in grain-size and void-size distribution for ceramic molds used for bronze casting technology from Panlongcheng and Yinxu, as most technological analyses of bronze casting have focused more heavily on identifying metallurgical composition. Lastly, in Chapter 8, I argue that the results indicate patterning reminiscent of a diasporic population that both maintained and developed cultural practices in terms of raw material selection, cooking technology, firing temperature and mold making that reflect a distinct Panlongcheng identity. These results highlight the need for further explorations into ceramic technology across the Erligang landscape to better understand the complex processes of continuity and change unique to each individual site.7 Chapter 2: Diaspora: An Identity of Dual Consciousness The term diaspora originates from the Greek dia (“across”) and speiro (“to scatter”, as in the scattering of seeds). At its most basic, the concept of diaspora in anthropology and archaeology is the scattering and dispersion of people across space from a “homeland” to somewhere new. Diaspora theory as an interpretive framework is also about continuity, persistence and change, and the creation and maintenance of an identity for these dispersed groups as they encounter and live among other populations. As these groups settle in their new environment, they maintain practices, rituals and memories of their home while gradually developing or incorporating new practices and rituals of their own. Social boundaries between this group and newly encountered populations “emerge out of the distinctly liminal social status of their constituents who are of one place and in another”, and over time it is possible that “diasporic communities’ unique experiences and values might set them apart in the home country, perhaps serving as grounds for a new social distinction or even ethnic boundary” if they were to return or encounter populations from their place of origin (Cipolla 2017:3). Abner Cohen’s (1969) discussion of “trade diaspora” is an early anthropological example that emphasizes the duality of diasporic groups, noting that “its members are culturally distinct from both their society of origin and from the societies among which they live” (267). In a trade diaspora, merchants disperse along trading routes and settle in host communities while developing and maintaining interregional exchange networks with each other. Cohen’s analysis explores the various processes involved in maintaining a trade diaspora (e.g., demographic replacement, regulation of trust between groups, organization 8 of authority over extensive geographic regions), specifically Hausa merchants interacting with various Yoruba communities in Nigeria and other areas in west Africa. Cohen argues that the maintenance of a cohesive, unique identity amongst the traders (distinct from both their place of origin and their host communities) allows the exchange network and diaspora to sustain itself despite the distance, the lack of central authority, communication hurdles and other such challenges faced by a dispersed but connected group. Gil Stein (1999) adopts Cohen’s framework to counter the predominance of world-systems theory/core-periphery models (which, as noted above, has been a persistent model in interpreting Panlongcheng’s role in Erligang). Stein views the trade diaspora model as a flexible framework that “allows for the roles of individual agency and multiple forms of social identity as key factors affecting political economy and development pathways” (45). Stein reiterates that in a trade diaspora, which he is careful to differentiate from “the more general notion of diasporas as transnational communities”, the trading group maintains a separate cultural identity from their host community while functioning as “intermediaries or cross-cultural brokers between their host community and the outside world” (47). According to him, this maintenance of a cohesive identity helps deal with the balance of power across interregional networks and interactions. Stein uses 4th millennium Hacinebi, Turkey as a case study, arguing that Uruk tradespeople settled alongside the native Anatolian population due to Hacinebi’s strategic position along the Euphrates River (along which numerous other trading posts were established). There is evidence of the Anatolian population existing and trading pre-9 contact (4100-3700 BCE), and a full-range of Uruk material culture suddenly emerging in the contact phase (~3700-3300 BCE). This included cylinder seals, which are Uruk-style administrative artifacts used to monitor the flows of goods, that contrast with the Anatolian-style stamp seals used for a similar purpose, suggesting that the Uruk population monitored their own flows of goods across their exchange networks. Stein argues that since the Uruk materials are spatially distinct from the Anatolian deposits of the same timeframe, the Uruk minority was able to live alongside the Anatolian population while maintaining its own distinct identity that allowed for the maintenance of the Uruk community along the trade route. Cohen and Stein provide key concepts for conceptualizing diaspora in anthropology and archaeology, but the narrow focus on trade and the maintenance of identity for macro-scale dynamics limits its applicability to other situations of migration and relocation and to other scales of analysis. Narrow definitions or applications of diaspora are in fact relatively common in the early literature. Safran (1991) for example listed several defining qualities, including a feeling of alienation in their host communities and the desire for an eventual return to and/or restoration of the homeland (83). These qualities, however, were based on very specific postcolonial populations - Safran talks of the Jewish diaspora (the forced exile of the Jewish population from the Land of Israel to various other settlements around the world) as the paradigmatic example, and offers discussions of other diasporas (e.g., Turkish, Chinese, Palestinian) as diverging from this paradigm in various ways. Along with the Jewish diaspora, diaspora in anthropological and archaeological literature is also most often associated with the Black Atlantic - a distinct culture 10 formulated by interweaving multiple others after the movement of peoples from Africa to lands around the Atlantic Ocean (i.e., the Caribbean, Britain and the Americas) during the slave trade in the mid-16th century. The Black Atlantic as an identity was first conceptualized by Paul Gilroy (1993) in “The black Atlantic: Modernity and double consciousness”, wherein he describes the multifaceted, multinational Black identity that arose from the shared experience of enslavement and the othering of Black populations as western nationalism became tied to whiteness. As such, much of the early (and current) archaeological literature on diaspora was borne out of historical archaeological analysis of this dispersed population, first starting with the search for “Africanisms” - material culture, mostly ceramic typologies, that harkened toward practices originating in African homelands (Gartley 1979; Heath 1999) - and later approaches recognizing that diaspora can manifest materially in other ways such as spatial contexts (Finch 2013) or foodways (Wilkie and Farnsworth 2005), even when signature typological markers are less obvious. According to some, such as archaeologist Ian Lilley’s influential 2004 chapter “Diaspora and Identity in Archaeology: Moving beyond the Black Atlantic”, the connection and possible conflation of the word diaspora with the Jewish and Black Atlantic populations led to a negative and limited connotation of the term. They have argued that even earlier usages of diaspora actually conveyed some level of positivity and that the narrow interpretations are a more recent phenomenon that are not necessary for a diasporic population (Cohen 1997:2; Dufoix 2018; Lilley 2004). It appears that the strong associations with both the Black Atlantic and Jewish exiled populations to the concept of diaspora long kept scholars from applying the term to other groups and circumstances. 11 This is not to downplay the critical importance that scholars of the Black Atlantic and Jewish diasporas had on the understanding of diaspora theory. Stuart Hall’s (1990) crucial essay “Cultural Identity and Diaspora” argues much of what is now common in anthropological and archaeological theories of identity - that identity is not so much a fixed entity, but rather a “production” (257) and “positioning” (261). As he notes, “cultural identities come from somewhere, have histories. But, like everything which is historical, they undergo constant transformation” (Hall 1990: 260). Hall’s concept of diaspora draws on the Derridean concept of différance, a play on French verb différer or to defer in combination with différence. The central significance of différance to diaspora is the constant deferral of meaning, and signification arising only in contrast to other signs. You see the distinction between différence and différance in text or with contextual clues. Identity similarly is constructed not through inherent qualities but in contrast to other identities; as such, the identity of a diasporic population is constantly “repositioned in relation to different points of reference” (Hall 1990: 227). A person with connections to more than one place may therefore feel like an outsider and an insider simultaneously; different compared to whomever they are in contact with. While maintaining practices of their homeland, they inevitably pick up new practices of their hosts. As a result, to those from their homeland, they will seem like the “host community”; to the hosts, they will seem like the homeland. The nature of diaspora is related to topics more commonly covered in archaeological literature like migration, culture contact and colonization. The most common thread through recent literature on these topics is the movement away from 12 acculturative perspectives that see an intrusive, dominating population entering an area with their own material culture, which is then uniformly adopted or imitated by the existing populations in that area. As an example of acculturative methodologies, Lightfoot et al. (1998:200) note early studies of Native American archaeology that quantified the proportion of European versus Native American goods as a representation of the level of assimilation that occurred over time. The result, according to acculturation theory, is that smaller, less powerful societies undergo far more cultural change when in contact with an intrusive population, losing their cultural identity as they adopt the cultural practices (and resulting material culture) of this dominant group. Archaeological research has largely moved away from these models with a growing awareness of their overgeneralizations and the historical contexts in which they arose. Critics have noted that these models were inspired, influenced or directly based on more recent societies immersed in the global capitalist system that cannot be directly compared to societies that existed before or outside of it (Rice 1998; Schortman and Urban 1998). Even in the case of societies directly impacted by capitalism, these models rarely account for the resistance, contestation and persistence of societies in the face of colonization or culture contact, as well as changes occurring within the colonizing groups. The models also tend to view cultures as static, bounded entities defined by particular material traits, when the reality is much messier and more dynamic. While there is no uniform theory of colonization and culture contact, numerous studies now focus on the active nature of identity-making via social boundaries (Stark 1998), notions of hybridity (Deagan 2013; Silliman 2013) and creolization, particularly in contexts of “frontiers” (Hu 13 2013:389; Shelach 2009), “peripheries” (Rice 1998) and “borderlands” (Cusick 2000). As with Hall above, these topics de-emphasize static understandings of what one “is”, focusing instead on the active and continual process of identity-creation. Archaeologists now recognize that identities cannot simply be inferred through typologies or distribution alone, and that identities can be multitudinous, contradictory and fluid. It may be tempting to wonder how diaspora differs from concepts like hybridization (“the creation, through interaction and negotiation, of new transcultural forms, ambiguous cultural affiliations, and ambiguous meanings” (Deagan 2013: 261)) or how one distinguishes diaspora from colonization, particularly in the analysis of archaeological material. However, diaspora is not necessarily distinct from either concept. Instead, it can be seen as a framework capable of incorporating concepts like hybridity (or other forms of combining) into its analysis. The incorporation and mixture of “typical” material traits and practices from other groups is a marker of hybridity that can arise in the context of the diasporic condition. In the same vein, a diasporic population is not necessarily distinct from a colonizing population; as argued above, a diaspora does not need to have been forcibly exiled. The group(s) in question may have instead been required or motivated to leave due to other factors such as state-scale expansion, poverty, a desire for fresh beginnings, or all of the above. As such, a population can simultaneously be colonizers and diasporic, an intrusive group that nevertheless retains memories of a homeland while incorporating new practices and rituals. In fact, the struggles and adaptations of colonizing populations is a perspective that some scholars have argued has been relatively underexplored, prioritizing 14 instead the perspectives of the colonized alone (e.g., Gosden and Knowles 2001; Voss 2015: 664). This can ironically reinforce a simplistic colonizer/colonized dichotomy, where the colonized group resists (instead of assimilates) the colonizers’ influences but the colonizers once again are relatively static and unimpacted. The diasporic framework has the potential to undo this stubborn dichotomy by examining processes of continuity and change in any group, colonizer or colonized, and the mutual impact such groups have on each other. Related to the concept of identity-creation is the recently popular study of “ethnogenesis” - literally, the creation of ethnicity. Like diaspora, the concept of ethnogenesis has moved from a more restrictive model (i.e., attempts at discovering more discrete forms of new ethnicities) to “a dynamic model of identity formation that encompasses both change and continuity” (Voss 2015: 657). Voss however cautions that this definition is perhaps too broadly applied and that the study of ethnogenesis requires rigorous investigation, specifically whether ethnic identities were important and whether they were substantively transformed. There is a great deal of literature, beyond the scope of this dissertation, that debates the definition of ethnicity. Voss (2015) summarizes it as “’a consciousness of difference’” (as per Vermeulen and Govers 1994) “that is negotiated both through external debates about differences between ‘us’ and ‘them’ and through internal contests over community self-definition” (658). Such a definition has obvious overlap with the Derridean concept of diaspora outlined above, or Gilroy’s (1993) discussion on “Double-consciousness” in the African diaspora. The main difference in conceptualization is one of 15 origin: where diaspora applies most commonly to a situation of dispersal, ethnogenesis can arise out of numerous environments and contexts. As outlined by Hu (2013), situations like internal fissioning, resistance, unequal access to power and/or the maintenance of social inequality can also lead to ethnogenesis. Cipolla (2014) and Malouchos (2020) argue that there is an inherent ethnogenic process in diasporic situations, and that “ethnogenesis in diasporic contexts can be understood as the emergence or transformation of new social identities whereby spatially incongruous communities negotiate the making and remaking of collective identities outside of, but in reference to a homeland” (Malouchos 2020:132). From this perspective, the new identities that arise for dispersed communities out of both the maintenance and production of practices, and in contrast to other social identities, are a form of ethnogenesis. They both also argue, following Harris (2014), that non-humans also play a critical role in the development of new identities. Communities are formed not only from shared symbols and practices, but also via shared experiences from and engagements with the environment, including geological landscapes, animals, flora, and other “things”. Ethnogenesis from diaspora, then, is not simply based on encounters with each other but also encounters with everything else in the world. The active, transformative, generational process of learning and knowledge acquisition about a new environment is a fundamental part of colonization and settlement (Rockman 2003). People take with them the knowledge they have from their origin and apply it to their new surroundings, gradually incorporating new experiences and understandings as they learn the similarities and differences of their 16 homeland. The very act by diasporic communities of relocating to a new environment, learning about and using different resources will therefore have a significant impact on group identity and the delineation of social boundaries. 2.1 Diaspora, Identity, and Archaeological Material Exploring an ancient culture through the lens of diaspora means attempting to see how a group identity was both maintained (in reference to a homeland) and changed over time. The ambiguous nature of identity, however, can make comparative studies and analyses of identity difficult. The general consensus at this point is that typological distributions cannot alone signify group identity. But if similar things - pottery, bronzes, weapons, burials - are not in and of themselves representative of identity, then what about social identity and social boundaries can we really glean from archaeological material? Much of the literature on diaspora, hybridity, social boundaries, ethnogenesis and other related topics come back to practice theory as outlined by Bourdieu (1977) and structuration as per Giddens (1984) (cf. Hu 2013; Finch 2013; Frankel 2000; Lightfoot et al. 1998; Stark 1998; Voss 2015). Since these topics and frameworks reject the concept of culture and identity as a bounded and static entity, and instead see them as active processes, it is exploring not just what people do but how they do them that is critical to understanding identity. Exploring the how in archaeology is often linked to technology and what is known as “technological style” (Hegmon 1998). In response to previous conceptions of technology as an “extrasomatic means of adaptation”, numerous scholars now view technology as 17 behavioral, as a way of doing things, and as “a dynamic web weaving an unlimited number of factors, or threads, into a seamless whole” (Binford 1965: 205; Dobres 2000: 61, 96). In order to parse these behaviors and mindsets, objects can be viewed as part of a technical system or operational chain (chaîne opératoire) from beginning to end – e.g., raw material procurement, production, use and discard (Lemmonier 1986). This system, or “dynamic web”, is comprised of a series of choices that the artisan must make based on a multitude of constraints, including environmental, economic and utilitarian factors, but also political, social and cultural considerations as well. The cumulative, unique combination of choices that lead to the creation of an object within a technical system can be said to represent its technological style (Hegmon 1998). Different regions, cultures and/or social groups may show variations in technological style based on their unique historical social processes and cultural values. Conceptualizing technology beyond the adaptive, and style beyond the decorative, can therefore aid in the identification of unique social behaviors in the process of production and use. Practice theory and Bourdieu’s habitus become a useful lens for understanding the various processes of stability and change in technological style. Habitus refers to the repetitive actions and embodied practices of people with a shared culture or social reality, reflecting the ways in which historical and social processes and cultural norms shape the ways in which people act on a regular basis. Certain actions for one group may feel “unnatural” to another because of unconscious habitual action – for example, forms of eating (e.g., with hands or with utensils), speaking with strangers (e.g., with or without personal space), or communication styles (e.g., directly or implicitly). Technical systems 18 are also comprised of habitus in the form of routinized gestures, preferred material selection and other factors. Ethnoarchaeological research has shown that these technical systems can be incredibly robust, even in the face of alternatives, because of the ways in which knowledge is passed on via active, embodied learning (cf. Gosselain 1992). As a result, the actions behind making objects become embodied habits that are difficult to change, particularly if other steps within the technical system would also have to change. If one were to change the type of clay acquired for cooking pots, for instance, this might have great impact on the workability of the clay, the gestures required for the formation of the pots, the firing processes, and the desired usage. That said, the concept of habitus does allow for change. As noted previously, technical systems and technological styles comprised a series of choices based on a variety of physical and social constraints, which then become embodied as habitus. However, different choices can be made within these constraints by agents (i.e., experimentation), and different choices may have to be made if the constraints change (e.g., economic need, relocation). If some of these choices yield positive results and become widely accepted, the steps in the system – and habitus – change. Habitus is therefore not a static concept but is instead a “generative principle of regulated improvisations” that offer a useful lens for understanding why processes of stability and innovation occur in technological systems (Bourdieu 1977:78). Archaeological applications of Bourdieu’s concept of habitus, often integrated with chaîne opératoire, technological style, and other analytical frameworks, have become increasingly common. An influential example is Dietler and Herbich (1998)’s 19 ethnoarchaeological research among the Luo people in Western Kenya. Their research focuses on Luo pottery production and the social relations that influence the production process, identifying “micro-styles” that manifest in neighboring potter communities. Dietler and Herbich note that each community has “characteristically patterned permutations of technical, formal, and decorative attributes” that persist through domestic contexts of learning (often mother-in-laws and co-wives teaching women marrying into the community) (Dietler and Herbich 1998: 250). These production techniques however are not dictated by rigid rules, but are tendencies based on “perceptions of an acceptable range of variation in choices at different stages of the chaîne opératoire” which can be adapted in response to different demands (such as historical trajectories, economic factors, material availability and so on) (Dietler and Herbich 1998: 250). Meanwhile, due to distribution networks, the objects themselves may end up in territories unrelated or even hostile to their sense of identity. Thus formal style and typological patterning on its own is not necessarily the best proxy for identifying social boundaries, which appear to manifest as habitus in the social context of production rather than the formal qualities of the materials themselves. Their research also incorporates analysis of architectural forms and draws similar conclusions. The shape of domiciles changed from round to rectangular, though not uniformly, across Luo territories as an adaptation and adoption of European styles. However, this adoption does not necessarily indicate a shift in identity and acculturation that one might expect from early models of colonization. Instead, Dietler and Herbich argue that the unchanging spatial organization of the interiors, where habitus of social 20 relations related to daily life take place, are representative of the persistence of Luo identity despite colonial influence. Additionally, they argue that changes in the construction materials had more impact on social relations than the house shape, as there was a traditional gendered labour division in terms of material usage, and class differences reflected in changing material usage as well. Both examples highlight how identity can be embedded in practice and routinized actions, while typological and formal qualities alone can be misleading as a proxy for social boundaries. Stark et al. (2000), in an ethnoarchaeological study of two neighboring villages in the northern Philippines, drew similar conclusions when analyzing the mineralogical composition of ceramic pastes. The two villages, Dalupa and Dangtalan, had experienced different historical and economic trajectories that led to a divergence in pottery production practices. According to earlier ethnographic interviews, both villages had preferences for clay sources based on workability, geographic proximity and also social and political considerations (Aronson et al. 1994). By analyzing ceramic samples from both villages, Stark et al. (2000) found that while the general paste characteristics from both sites were similar, there are still discrete compositional differences (e.g., enrichment of volcanic fragments, differing quantities of plagiocase and hornblende) that reflect differences in clay sources and potential differences in clay processing techniques. The study not only validates the differences explained in the ethnographic interviews but highlights how differences in the chaîne opératoire borne out of cultural preference and habitus can even manifest and reflect social boundaries in groups with such geographical proximity. 21 Both of these studies use habitus and chaîne opératoire to explore social boundaries embedded in practice related to neighboring and somewhat interrelated communities. However, these concepts have also been used to explore cultural continuity and change in the face of relocation and culture contact. Lightfoot et al. (1998) for example analyzed spatial structures and trash disposal practices at Fort Ross, a colonial outpost 110 kilometers north of San Francisco Bay, in order to explore the process of identity negotiation of Native Californian women living in interethnic households with Native Alaskan men. The men were brought to Fort Ross to harvest sea mammal furs and to serve as laborers while the women would leave their homeland villages to settle in Fort Ross with these new partners. Lightfoot et al. (1998) observe that while the spatial structure on the Fort Ross landscape was colonial in nature, the individual neighborhoods within the town were relatively flexible and reflected Alaskan (Alutiiq) principles of spatial organization. Meanwhile, within the individual households, Native Californian women were able to reproduce their traditional daily practices and cooking techniques and use Native Californian material culture within their domestic space. Despite maintaining these practices, however, Lightfoot et al. also remark that “cultural transformations took place as Alaskan men and Californian women responded to one another and accommodated themselves to a new social setting” with new foodways, use of new raw materials in the productive of native artifact forms, and innovations in landscape modification and refuse disposal (Lightfoot et al. 1998: 216). While Lightfoot et al. (1998) don’t explicitly use the term “diaspora”, the experiences of both the Native Californian women and Native Alasken 22 men align with those of diasporic populations. By analyzing Fort Ross at different spatial scales, Lightfoot et al. are able to highlight the ways in which both groups maintain practices of their homeland while negotiating and constructing a new identity in the face of new social and environmental circumstances. Wilkie and Farnsworth (2005) more explicitly investigate diaspora among the people of Clifton Plantation, a colonial population in the Bahamas, using an integrative framework of habitus, Giddens’ (1984) structuration and Butler’s (1990) performativity. According to the authors, much of the “Bahamian” identity that is present today is a result of “cultural compromises negotiated between enslaved, free, and liberated African peoples” that accomplished a “sense of communal harmony while also maintaining a series of distinct social and cultural identities” (Wilkie and Farnsworth 2005:1). Their work takes advantage of both historical documents and archaeological material at different scales of analysis (architectural/spatial layouts, foodways, household assemblages) to explore how the diverse population at Clifton Plantation maintained homeland practices over two generations alongside the development of a new identity borne out of shared tensions and mitigative practices such as communal feasting. Wilkie and Farnsworth’s (2005) work, like that of Dietler and Herbich (1998) and Lightfoot (1998), emphasizes that analysis at different levels can reveal different “truths” about dynamics of cultural identity. They argue that analysis at multiple scales in conjunction with theories of practice, agency and structuration allow for the most flexibility in interpretation and understanding. Being zoomed out to the macro level, whether this be the spatial organization of a site or the formal properties of ceramics, may 23 reveal something different compared to the “micro” level reconstructed via the analysis of habitus and/or chaîne opératoire - like household organization or ceramic microstructure. As Hegmon (1998: 278) cautions, it is not necessarily that the micro level (in her case, technological style) is a better avenue for exploration than the macro level (formal qualities). Rather, as emphasized by all the authors above, investigation at multiple scales will help us understand the simultaneous and potentially conflicting dynamics that occurred in the past. Considering the different and coexisting ways in which identity can be constructed, contested and reinforced, particularly in contexts of diaspora, researchers should not simply rely on just one avenue of evidence to determine overarching social dynamics of these complex societies. When it comes to the Erligang period, I am not the only person who has considered the idea of a diaspora. In his essay, “China’s First Empire”, Wang Haicheng (2014) flirts with the idea of Zhengzhou, Panlongcheng and other associated sites as a trade diaspora, comparing and contrasting the situation at Panlongcheng to Gil Stein’s (2001) analysis of Hacinebi in Turkey. Like Hacinebi, Panlongcheng has often been theorized to be a strategic outpost due to its proximity to copper mines, and it is clear there was a pre-existing population there (Erlitou) as well as the sudden appearance of new material culture (Erligang). However, unlike Hacinebi where the new material culture persisted and co-existed alongside that of the pre-existing population, there is no clear evidence for hybridization or coexistence of Erlitou and Erligang simultaneously. Wang therefore believes that the trade diaspora model is insufficient and instead argues that a forceful settlement displaced and/or dominated the previous populations rather than living 24 alongside them. He concludes that Erligang was structured more like an empire, with its elite more heavily focused on domination and ideology, as opposed to other models of expansion. However, his reasoning only indicates that Panlongcheng was not a trade diaspora in Stein’s limited definition, which Stein notes should not be conflated with broader definitions of diaspora like those discussed above. It makes sense that Wang would focus on trade diaspora as this model tends to concentrate on macroscale processes, which is in alignment with his aim of identifying the broader goals of Erligang expansion. However, since this study focuses more heavily on bottom-up processes and local dynamics, I purposely avoid narrow definitions and characteristics of diaspora as research on modern and ancient populations and what constitutes a diasporic population continues to shift and grow. As evidenced by Safran’s (1991) and Finch’s (2013) research, one may conceptualize an paradigmatic definition of diaspora but it is inevitable that groups will diverge from this ideal due to the unique circumstances experienced by each migrating population. Instead, this research focuses on diaspora in conjunction with habitus, chaîne opératoire and technological style, exploring both the stability and change in ceramic production in Panlongcheng, and how these changes may reflect identity-creation of Panlongcheng residents as they settle into a new environment. Before delving into the analysis, the following chapter contextualizes the discovery of Panlongcheng, which has played a crucial role in its representation in the literature. With recent excavations, researchers have begun to focus more on the differences, rather than the similarities, of Panlongcheng material culture compared to that of the Central Plains 25 and in doing so the dynamism and complexity across the entirety of the Shang site network has come to the surface. 26 Chapter 3: Panlongcheng Research History and Material Culture Panlongcheng (~1500-1250 BCE) is a site near modern-day Wuhan in Yedian village, Huangpi, Hubei, China. It sits on the north bank of the Fuhe River, a tributary of the Yangtze River, and sits at the edge of Lake Panlongcheng (Figure 1). The site was discovered in 1954 during flood-control projects and has been consistently excavated since 1974. These excavations have revealed city walls, large building foundations, elite tombs, over 350 bronzes, jade, pottery and stone artifacts and likely pottery and bronze casting workshops (Bagley 1977; Zhang 2014; Zou and Zhang 2020). Figure 1. Panlongcheng site areas. Panlongcheng’s discovery was, and still is, remarkable in the context of early Bronze Age archaeology in China. When Panlongcheng was discovered, Yinxu - the last 27 capital of the Late Shang dynasty (~1250-1045 BCE) - had already been nearly continuously excavated for 26 years. The materials at Yinxu, located in modern-day Anyang, Henan (about 550 kilometers south of Beijing and 700 kilometers north of Panlongcheng), revealed a complex and deeply stratified civilization with a robust bronze-casting industry, horse-drawn chariots, massive royal tombs, and oracle bones, the first extant writing in Chinese history. Figure 2. Erligang distribution map – select sites. Created by Scott Humphrey. As Bagley (2014) details in a research history of early Bronze Age China, the scholarship at the time assumed that the Near East was the birthplace of civilization and as such, the material culture of Yinxu was presumed to have been influenced by that region and 28 Neolithic cultures in the area. Moreover, at one area of the site there was Bronze Age Anyang layer directly over a Neolithic layer, with no prior bronzes - this sudden appearance made it seem as though Yinxu’s appearance was an abrupt event and the origin of Chinese civilization (Bagley 2014: 26). In 1951, the discovery of pottery of the Erligang typesite, also in Henan, meant that this original assumption required reevaluation. The pottery appeared similar, but older, than that at Yinxu. Four years later in 1955, remains at Zhengzhou were uncovered, including a city wall with a circumference of seven kilometers (Bagley 2014: 29). The pottery characteristics shared with the Erligang site, coupled with the size of its city walls and a few tombs unearthed containing bronzes (which were also stylistically identified as earlier than Yinxu bronzes), indicated an important site preceding the existence of Yinxu. Further excavations at Zhengzhou have also uncovered palatial complexes, ritual bronzes, bronze foundries, jades, and ceramics, continuing to solidify its relevance and complexity, though notably no oracle bones or royal tombs have been found there (Bagley 2014: 31; Zhang 2014: 53-54). The unfortunate reality about Zhengzhou is that most of it is underneath the modern-day city of the same name and full excavation of the site is essentially impossible as a result. However, the remains available indicate that Zhengzhou began as a gathering spot of many different groups, with the Erlitou culture co-mingling with the Xiaqiyuan, Yueshi, Huiwei and Dongxiafeng cultures (Hou 2024). Due to its strategic and convenient location near multiple rivers and basins, Zhengzhou was able to connect with the north and west through the Yellow River, the eastern region through the Ji River, with the Jianghan 29 region via the Nanyang Basin, and the Jianghai in the Southwest via the Ying tributary of the Huai River. As such, the establishment of Zhengzhou was a prime opportunity for multiple groups to come together in an advantageous location near mountains, water and abundant resources. Over the course of the establishment of Zhengzhou, the scale of the city grew larger, the building structures became increasingly more complex, and its material culture became more standardized over time (Hou 2024: 259). While the remains at the beginning clearly showed signs of cultural diversity, such material culture (bronzes, jades, pottery, burial practices, and building structures) became more standardized as a clear identity evolved. The city established several areas of craft specialization, large-scale production and institutionalized rituals (i.e., the development and standardization of Erligang-style bronzes) (Hou 2024: 259). In the later stages of the Erligang period, Zhengzhou became less prominent with the rise of the nearby Xiaoshuangqiao site as the new capital city, but similar practices were carried over to the new site and Zhengzhou remained a “sacred homeland” even after the shift (Hou 2024: 4). During the height of Zhengzhou, there were numerous settlements near the capital. Of note is the large center of Yanshi Shangcheng, located 6 kilometers northeast of Erlitou and 105 kilometers west of Zhengzhou, with palace-temple areas, bronze foundries, pottery production sites and rammed-earth walls (Campbell 2014a: 74-77). These two sites are part of a tradition Campbell (2014a: 79-80) calls the “Central Plains Metropolitan tradition”, which, as noted above, combines the material culture of Erlitou with various other traditions – primarily from the east/northeast, but also from the south and elsewhere 30 as people migrated to the center. As with Zhengzhou, the material culture at Yanshi gradually developed into something more homogeneous, a process of urbanization also witnessed during the rise of Yinxu (Jing et al. 2013). Many other Erligang sites scatter the landscape (Figure 2 for select sites), which Campbell (2014a: 70-104) grouped into several different regional variants (see Campbell 2014a: 70 for a full map). These variants overlap with the Metropolitan tradition but are shaped by their local environments, pre-existing populations, their own local traditions of the preceding era (Erlitou), and interregional interactions, resulting in their own unique characteristics. Some have more in common with the Metropolitan tradition than others. For example, the Liulige variant in Henan strongly overlaps in ceramic assemblage and bronze style, representing “a strengthening of existing similarities indicating more intense interactions centered on Zhengzhou” (Campbell 2014a: 80). The Daxinzhuang variant to the east in Shandong province shows incorporation of Erligang li-cooking vessels but a predominance of Yueshi yan-steamers, as well as a mixture of Erligang and Yueshi stone tools. Meanwhile, the Dongxiafeng tradition features both Erligang and regionally traditional tools and artifacts but does not appear to have adopted Erligang-style waist pit burials (discussed more below), indicating some intermixing but a maintenance of local ritual practices (Campbell 2014a: 82-83). There was also an expansion to the south into the Jianghan Plain, especially along the Yangtze River and its tributaries. Considering the prevalence of sites situated atop hills or, in some cases, sites that were deliberately constructed to be located on higher ground, Zou (2022) suggests that the Erligang expansion had military strategy in mind. Based on 31 ceramic typologies, the spread of Erligang material culture along the Jianghan Plain was accompanied by integrations with local materials. A combination of Erligang remains and local factors and innovations can be seen across settlements such as Zaoshi and Pagoda along the Li River, or Jingnansi and Lijiatai where indigenous materials account for >50% of the cultural remains at each site (Zou 2022: 82). One of the most important Erligang sites in the Jianghan Plain is that of Panlongcheng, which contains a mixture of both the “Metropolitan tradition” and its own distinct material characteristics. Since its discovery, Panlongcheng has been subject to much attention due to its location and connection to the Central Plains, as well as its walled center and its wealth of elite goods. Due to the limitations of excavating Zhengzhou, the elite material found at Panlongcheng – including the only elite tombs of the Erligang Period unearthed thus far – has become a critically important source of information on Erligang hierarchy and has drawn attention due to the residence of elites in such a unique, distant location relative to the other Erligang centers. The framework for interpreting Panlongcheng initially tended toward the core-periphery model, with great focus on its role in service to Zhengzhou rather than its own internal dynamics or its place in a broad interconnected network of sites. The interpretation and discussion of Panlongcheng as an outpost for the gathering metallurgical materials for bronzes is the primary example of this (cf. Bagley 1977; Liu & Chen 2001; Wang 2014). Based on the material evidence, there is a clear connection between Panlongcheng and Zhengzhou. But the nature of this connection is less obvious. As noted, there has been a tendency to overemphasize the large centers in the Central 32 Plains as a locus of domination and control, assuming a wholesale political and cultural influence over their peripheries. This tendency, Li (2014) argues, is based on a dynastic model represented in historical texts, subsequently carried over and reproduced as an archaeological framework. The end result is that “interactions between Anyang and other polities are therefore explained only in terms of political ties, and often within a scheme of ruler and ruled” and that “more sweepingly, it has been proposed that the distribution of bronze ritual vessels across China during the early Bronze Age identifies the domain of a common culture that became the foundation of the Central Plain-based Chinese civilization” (Li 2014:142). Li (2014) argues that we need to know more about other sites, and that it is more realistic to view state-level societies such as Erligang and Anyang as “multifaceted, multiethnic, and metropolitan by nature” (143). Since there are “no simple one-to-one correlations between types of material remains and types of interaction”, it is important to evaluate the material remains and the dynamics of each site on their own terms before making any assumptions about site dynamics (Li 2014: 143). He is not the only one to argue for this perspective. David Keightley (1983) posited that, rather than a uniform and overarching authority, the late Shang state is more of a network, with “intermittent” or “transitory” state power. And while not framed within the same critique of Central Plains-dominance, Jing et al. (2013) argue that “relatively little discussion is focused on the actual meaning and dynamic nature of cities that may show substantial variation in their workings from place to place and over time” (386). They also note an increase in research on the “interactions of early urban centers with their surrounding landscapes at a regional scale”, 33 but state that regional perspectives “must be complemented by the study of the internal organization and dynamics of urban settlements” (386). While the connection between Panlongcheng and Zhengzhou has been strongly emphasized since its discovery, Panlongcheng’s material culture, particularly its ceramics, has also been recognized to have unique characteristics. With increased and intensive excavations, particularly those between 2013 and 2018, the uniqueness of Panlongcheng and the complexity of its inner workings – not as site under the capital’s control but a self-sufficient regional center in its own right – has become increasingly clear. With evidence of local bronze casting capacity (Lu 2020), a potential stoneware and protoporcelain foundry (Xu 2018), terraforming projects specific to the area (Zhang et al. 2018) and fresh perspectives on Panlongcheng’s ceramic typology and technology (Chen and Jing 2020; Sun 2017), researchers are now building a more detailed picture of Panlongcheng and its local qualities. The purpose of this dissertation is to explore identity at the site of Panlongcheng through the lens of diaspora and ceramic technological style, not only to shed light on Panlongcheng but also Erligang identit(ies) in general. Given the variability and distribution of Erligang sites and the seeming dispersal of people with shared but localized practices, the diaspora framework discussed in Chapter 2 can help guide further research into the development of site “identities” and interregional interaction. This is not necessarily to classify certain sites such as Panlongcheng as “diasporic/non-diasporic”, but to highlight and consider processes underlying unique practices and identities at different sites that can be investigated using multiple levels of analysis. Campbell’s efforts to assess and 34 classify Erligang sites based on their own unique material traits has been a great start, but in many cases and by his own assessment is often limited to formal ceramic and/or bronze typology (Campbell 2014a: 87). Further exploration into its ceramic technology can help clarify the ways in which Panlongcheng residents were shaped by and adapted to external influences like the environment and neighbouring (or intermixing) populations, which in turn reflects the agency and habitus of its residents and the local technological styles that developed. With the ongoing connection between Panlongcheng and Zhengzhou, and the likely interactions and/or influences between Panlongcheng and other sites in the Southern network, the exploration of Panlongcheng’s ceramic technology can begin to fill in details and clarifications regarding the complex and dynamic network of interaction and exchange across the Erligang landscape. 3.1 Panlongcheng 3.1.1 Geographical and Chronological Context Prior to the Panlongcheng settlement, the Yangtze River area was populated by what is known as the Shijiahe culture, which declined around 2000 BCE. After the brief rise and fall of the subsequent Xiaojiawuji, the area was essentially uninhabited until the arrival of the Erlitou culture around 1700 BCE (Zhang 2020:54). Erlitou, named after its typesite in the Central Plains, is speculated to be the culture of the Xia dynasty and is the predecessor of Erligang. The Erlitou expanded southward from the Central Plains into Panlongcheng and 35 its material culture there represents “localized manifestation of cultural transmission”, indicating a relocated population from the north (Zhang 2020: 54). Panlongcheng is typically divided into seven phases based on pottery typologies. In the earliest phases (1-2), the pottery is essentially that of Erlitou in formal style, with some slight differences in vessel shape and/or colour. As Panlongcheng continued, there was an abrupt adoption of styles from the Central Plains alongside the development of some local styles, with a great deal of heterogeneity within styles in its middle phases. In the later stages of Panlongcheng, pottery types become more homogeneous (e.g., cooking vessels become more consistent in shape, size and style, as do other vessel forms), with an increase in more ‘local’ styles in terms of proportion and decoration. Alongside the changes in pottery, one can see the development in and around its city walls, expansion beyond its city walls during its most prosperous time (Periods 4-6), and the relatively swift decline in Period 7. More recently, radiocarbon dating has been carried out in the Panlongcheng areas of Yangjiawan and Xiaozui (see Section 3.1.2 for more information on these areas), which are associated with Periods 4-7 based on pottery chronologies (Zhu et al. 2020). Due to the distance between Panlongcheng and the Central Plains, pottery chronologies may not necessarily be in perfect alignment (e.g., there may be time delays as pottery types are gradually adopted), so these radiocarbon dates provide some clarity and precision in understanding the relationship between the two regions. Using charcoal samples from refuse pits in areas rich in pottery, the radiocarbon dates place the areas between 1500 BCE and 1230 BCE (Yangjiawan spans 1500 BCE – 1230 BCE, Xiaozui between 1450-1250 36 BCE) (Sun et al. 2020: 113). The initial ages of both Yangjiawan and Xiaozui fall between 1500-1450 BCE, overlapping with the second phase of the Erligang period in Zhengzhou. The construction of city walls and palaces in the Gongchengqu area can be dated to around the second phase of Zhengzhou as well, which is consistent with previous analysis based on pottery chronologies. However, the last phases of Yangjiawan and Xiaozui end around 1250-1230 BCE, which coincides with the dating results of late Huanbei and the first phase of Yinxu. Based on these dates, Panlongcheng was not fully abandoned until the development of Yinxu, instead of the abandonment of Zhengzhou as was previously believed. More sampling for dates in the earlier phases of Panlongcheng (1-3) still need to be done. 3.1.2 Geomorphology and Area Divisions Panlongcheng sits in the transition zone between the Dabie Mountains to the northeast and the Jianghan Plain to the southwest, in the southern part of the Huangpi District of Wuhan in Hubei Province, China. It is located on the north bank of the Fuhe River, the Yangtze River’s tributary, and sits in the alluvial plain on the edge of Panlong Lake. Unlike Zhengzhou and the rest of the Central Plains, which were characterized by a relatively flat landscape and a cool and dry climate during the Shang period (Ren et al. 2021), this area is covered in low hills and is humid with abundant vegetation. Currently around 50% of Panlongcheng’s total area is occupied by lake water but was only 15% at the time of Erligang occupation – as such, the land area settlement was about 3.24 km2 versus the 1.98 km2 that is visible now (Zou and Zhang 2020: 16). 37 The site is separated into several areas based on naming conventions by local residents, who have since been relocated by the government due to the ongoing archaeological work in the area (see again Figure 1). The area enclosed by city walls, otherwise known as the Gongchengqu (city palace complex), is situated to the southeast of the southern half of Panlong Lake, with a terrain that slopes downward gradually toward the south. This area also features moats and city gates, indicating its importance. A thin landform also extends outward from this area’s southern tip, a separate area designated Wangjiazui1 that closely followed the rise and fall of the city palace area. To the north of the city palace area, and the center of Panlongcheng, is Yangjiawan, which is situated on a natural hillock and runs 420 metres E-W and 310 metres N-S. This area has been continuously excavated since the 1970s, primarily in cooperation with construction and water-management projects up until 2006. Proactive excavation has been ongoing since 2006 after the relocation of local villages and the establishment of the Panlongcheng Site Museum. This area became the center of Panlongcheng’s settlement in its later stages (Periods 4-6) as the population expanded and required more space behind the confines of city palace area. To its east is Yangjiazui, extending into Panlong Lake, which also coincides with the time period of Yangjiawan. 1 The areas in front of the Panlong Lake area are designated “mouths” (zui) and areas further inland are designated “gulfs” or “bays” (wan) (e.g., Yangjiazui, Yangjiawan, Lijiazui, etc). Shorthand conventions to reference different areas are often found in the literature, i.e., PLZM2 (Panlongcheng, Lijiazui, Tomb (mù) 2. A systematic naming convention has also been established using quadrants that the site was partitioned into at the beginning of the 21st century, identifying the district number and the probe number (e.g., Q1712T0816 – quadrant Q1712, probe T0816). This dissertation will use the conventional names when using the area more broadly and will use the other two naming conventions when referencing samples. 38 Xiaozui is a narrow piece of land, with Pokou Lake on its east, west and south sides, that extends southward from Yangjiawan. It sits east of Gongchengqu and was likely connected to Gongchengqu’s landmass during the Erligang period when water levels were much lower (Zou and Zhang 2020: 12). The cultural accumulation is primarily distributed on its east and west sides of its hillock, with very little found at the top, though much of the area has unfortunately been damaged due to land-levelling that occurred in the 1960s. Lijiazui is a northwest-oriented hillock situated across a small body of water, to the east of Panlong Lake and north of Fuhe River. Like Xiaozui, Lijiazui was damaged when local villagers built embankments in the 1970s and 1980s to protect from floods. Nevertheless, excavations beginning in the 1970s onward have unearthed elite burials spanning phases 2 through 7. Panlongcheng has been excavated in patches, with a great deal left to explore (see map in Zou and Zhang 2020: 17). There are several more areas – e.g., Louziwan, Dadengzui, Xiaowangjiazui, Jiangiawan and Aijiazui – with limited fieldwork and/or limited finds thus far and have either been interpreted as peripheral areas or are currently less understood. Currently the most excavated regions are concentrated in and around the Gongchengqu (including Wangjiazui), Yangjiawan, Yangjiazui and Xiaozui. What has been excavated so far has been a wealth of material connecting Panlongcheng to the Central Plains, with clear evidence of building technology, pottery styles, burial practices, ritual bronzes and class stratification, all consistent with practices and materials in the Central Plains. However, further analysis and excavation work continues to clarify the unique traits, local industries and innovation that also developed in Panlongcheng. The following sections 39 explore the similarities and differences of Panlongcheng and Central Plains material culture in more detail. 3.2.3 Connecting Panlongcheng to the Central Plains In 1957, a few years after it was discovered, Panlongcheng was originally believed to be a Western Zhou (1046 BCE – 771 BCE) site because the excavators did not believe bronzes had made their way to the south so early (Zhang 2014: 52). However, the editor of the excavation report did note the similarities of the bronzes to those in Zhengzhou and additional bronzes discovered in 1963 solidified that they were indeed Erligang-style bronzes. Further finds in Panlongcheng continued to reaffirm that it was an Erligang site, with a report published in 1974 that established six key characteristics connecting Panlongcheng to the Central Plains: 1) Building technology, 2) Building orientation, 3) Burial customs, 4) Bronzes, 5) Jades, and 6) Ceramic assemblage (Hubei Provincial Museum and Peking University 1976). Rammed-earth foundations at Panlongcheng are built with the exact same technique as that in Zhengzhou. Like the city walls surrounding Zhengzhou, Panlongcheng is encircled with rammed-earth foundations and the foundations of its palaces (such as three within Gongchengqu), other large-scale buildings (e.g., six found in Yangjiawan, two in Yangjiazui, three in Wangjiazui, two in Xiaozui) and shaft tombs are similarly constructed. Soil is packed and pounded in subsequent layers between large wooden panels to form raised platforms, with the layers still clearly identifiable to excavators, upon which wood columns and thatched roofs are supported. Building foundations and tombs, 40 as is the case with the larger centers of the Erligang and Anyang periods, are typically oriented about 20 degrees east of north (Wang 2014: 85). Moreover, alongside orientation, burial customs are consistent with those in other Erligang sites. Burials are rectangular and feature a small pit underneath the waist of the interred that typically contains the remains of a dog, though human remains are later found in the waist pits of elite tombs at Yinxu. This burial custom is rarely found outside of Shang sites (Zhang 2014: 53). Bronzes and jades are also used in similar ways to indicate the status of the interred; for example, gu and jue-shaped bronzes are buried in pairs, and burials will also often contain jia or jade ge. In fact, elite wares are a major indicator of the connection to Zhengzhou and related sites, as the style and/or assemblage is nearly identical. In terms of bronzes, the decorative style follows a nearly identical course of development as laid out by Karlbeck (1935) and the jade assemblage stays essentially consistent from Erlitou onward (Campbell 2014a: 100). While casting and jade carving technologies are less malleable than ceramics (for example) from both a material and ritual perspective, there are sites like Wucheng that do feature idiosyncratic, local features for their elite goods. Lastly, the pottery assemblage in Panlongcheng overlaps with Zhengzhou and other Erligang sites. The rapid replacement of Erlitou guan flat-based cooking pots with Erligang-style li tripod cooking pots suggests a rapid population shift in the area (Wang 2014; Sun 2017; Zhang 2014) and, like both bronzes and jades, there is a continuous and synchronous appearance of new types in both Zhengzhou and Panlongcheng. 41 The cumulative evidence strongly indicates that Panlongcheng is an Erligang site, populated by residents from the north and continuously in contact with the sites in the north as well. There is little to no doubt at this point that Panlongcheng is indeed Erligang; however, the nature of Panlongcheng, its connection and “purpose” in the broader Erligang landscape continues to be discussed. There is growing evidence and discussion surrounding Panlongcheng’s local qualities and its course of development as an Erligang site with its own distinctiveness, alongside discoveries that question its originally theorized role as an outpost for metallurgical raw materials collection. The following section will elaborate upon recent excavations and the growing body of work regarding ritual bronzes and ceramics in Panlongcheng specifically, as this dissertation focuses on technological aspects of pottery and ceramics molds used for bronze casting. 3.2 Recent Excavations and Analyses 3.2.1 Bronzes Bronzes, as one might expect, have served a critically important role in our interpretation of Bronze Age China. Research surrounding the political economies of the Erligang and Yinxu periods have been based primarily around bronzes (and other prestige goods, to a lesser extent) and this in turn has played a large role in emphasizing the persistent top-down, unidirectional model discussed throughout this dissertation. The circulation of bronzes among the elite, extensive foundry remains in the late Shang capital of Yinxu, and the resources and labour required to produce these wares all paint a picture of a highly centralized political entity that focused heavily on resource acquisition and extraction to 42 reify its power. The consistency in style between Panlongcheng and Zhengzhou has similarly been cited as evidence of this model, and Panlongcheng was originally posited to be an Erligang colony built to send copper back to the capital where bronze production could take place. However, with analytical advancements and new discoveries regarding the bronze production process, there has been a re-evaluation of Panlongcheng and the role that it played regarding these wares. The bronzes of Panlongcheng are one of the primary pieces of evidence linking the site to Zhengzhou. They are often described along the lines of having “exact resemblance” to those Zhengzhou (Bagley 1977), with the surface designs aligning closely with Styles I through III as laid out by Karlbeck (1935), the vessel shapes themselves being in close alignment and the assemblage matching the types found in Zhengzhou exactly. Bagley (1977) points out that the bronze similarities between the two sites are even more stark when comparing them to local bronze industries that arose elsewhere such as Hushu (Jiangsu) or Xingan (in Jiangxi), both of which have idiosyncratic styles despite “Shang” influence. The consistency of bronze manufacture has been used as evidence for overarching political control radiating from Zhengzhou, the argument being that this level of consistency would be difficult to achieve without some sort of centralized authority (Wang 2014). In Wang’s view, pottery is a more malleable craft associated with “folk” culture, where bronzes and burials are highly ritualized practices that require conscious effort for alteration. As such, Wang views the distribution of ritual bronzes (as well as consistent 43 burial practices) as evidence of the expansion of an early empire controlling the production of these symbols of power. The association of bronzes with power - in the form of ritual (vessels) and warfare (weapons) - forms another crucial component to theories of Erligang political economy. K.C. Chang (1989) argued that the Chinese bronze age centered around the redistribution of bronzes and other prestige goods for the reinforcement of power, and that bronze production was therefore driven primarily by a political rather than economic purpose. Liu and Chen (2012) built a similar model of Erligang political economy, arguing that the defining characteristic of early Chinese civilization is the “elite’s obsession” with bronze vessels and their embedded ritual power, and that the “acquisition of this ritual power by elites… helped state rulers to obtain and maintain political legitimacy” (296). Along these same lines, the entire purpose of Panlongcheng’s existence was originally speculated to be related to its proximity to copper ores (Bagley 1977; Liu and Chen 2012). The central idea was that Panlongcheng was an Erligang colony built to send copper back to the capital where bronze production could take place, and that some of the bronzes and other elite goods were then redistributed back to Panlongcheng (and other outposts) to assert and/or reinforce the power of the elites who had colonized the region. More explicitly, Liu and Chen (2012) argue for a core/periphery model where “regional centers in the periphery provided raw materials and exotic goods as tribute to the core area” in order to promote growth and craft production in the capital, while elites in the periphery received prestige goods as rewards (284). Bronzes, presumed to be the primary 44 motivator for everything the elites did, are therefore central to the modeling of early Chinese bronze age political economies. As noted in earlier chapters, scholars have begun to question whether the evidence supports these models. Campbell (2014b) doubts the assumption that bronzes can be viewed as “indexical” of Erligang civilization, arguing that dominant interpretations are influenced by Central Plains-focused frameworks and Central Plains-focused research discrepancies. When taking into consideration the adopted technologies from elsewhere (e.g., chariots and horses, wheat cultivation) and the advanced technology present in the south (e.g., stoneware and thus pyrotechnical abilities), he argues that the “centralized control” model becomes less convincing compared to viewing various Erligang centers as “nodes in far-reaching and constantly changing networks of warfare, alliance, ritual, trade, tribute, marriage and other things” (Campbell 2014b: 128). In a later paper, Campbell et al. (2022:86) note that in Zhengzhou molds for tools in fact outnumber those for ritual vessels (n=71 vs. n=46 respectively) (citing Henansheng Wenwu Kaogu Yanjiusuo 2001:384), questioning the assumption that the bronze-casting industry was centered primarily around prestige goods for political power rather than practical economic purposes. While acknowledging that there very well could be more bronze workshops laying underneath the modern city, Campbell et al. argue that the current evidence does not support a view of Erligang state authority controlling bronze production primarily for ritual, or at the very least the practical and economic value of bronze production has been underemphasized. 45 3.2.2 Metallurgy Recent metallurgical research has suggested that the circulation of raw metal resources was not straightforward. Liu et al.’s (2019) analysis of the chemical and isotopic compositions of bronzes indicate differences in nickel, common lead, and tin levels. The authors postulate that metal cast at a nearby site of Hanzhong, which contained higher levels of nickel, were imported to Panlongcheng and re-melted, though more research is needed to confirm this hypothesis. The differences in common lead and tin meanwhile indicate the incorporation of local metal sources (and therefore local casting). There also appears to be a significant difference in the median silver levels between the Central Plains and southern sites (Liu et al. 2020). On the other hand, their copper levels suggest some shared metal circulation. The similarities and differences in metal composition between Panlongcheng and Zhengzhou bronzes therefore indicate that “the movement of metal in early Bronze Age China should be considered a multi-directional network” and that viewing Panlongcheng as simply a “transit depot” is an oversimplification of the Erligang situation (Liu et al. 2019: 423). Additionally, Shang period highly radiogenic slag (HRL) smelting slags were discovered to the northeast of Zhengzhou at Yingcheng, Laiwu, Shandong Province (Wang et al. 2023). HRL has relatively higher concentrations of 206Pb, 207Pb and 208Pb isotopes, and more than 60% of all analyzed Shang period bronze samples (>800) across various sites, including Panlongcheng, are characterized by HRL (Wang et al. 2023:2). Since the unique isotopic signatures of HRL can help clarify the circulation of bronze raw materials during the Shang, scholars have been seeking to find the source of this lead and have 46 typically been looking in areas rich in copper ores (including the middle Yangtze area around Panlongcheng) (e.g., Peng et al. 1999; Ying et al. 2020). The discovery of these smelting slags in Yingcheng, which had little archaeometallurgical focus previously, marks the first confirmed source of Shang HRL and adds another piece to the complex puzzle of the circulation of metals during the Shang period. However, it is currently unclear whether the resources in Yingcheng were rich enough to provide beyond Shandong or whether it was the main source to Daxinzhuang only, a large Shang regional center 60km north of Yingcheng with bronze workshops and other casting paraphernalia dating to the Middle Shang. It does appear that the source of HRL during the Shang was limited, as its usage does not extend beyond the Shang period. The discovery of Yingcheng’s smelting slags once again highlight the complexity of metal circulation during the Shang and the possibility of many different nodes in a network of interregional trade and interaction. At present, there has been very few discoveries related to mining and metallurgy near Panlongcheng, with some found only downstream of the Yangtze River to the southeast (Ruichang) and east (Tongling) during later periods. With multiple smelting sites dating from the Erlitou and Shang period discovered in the Zhongtiao Mountains (~280km east of Zhengzhou), prior even to the discovery of the HRL sites in Yingcheng, Zhang (2020: 55) concluded that Panlongcheng was not built as a copper-collecting outpost and speculates that it was instead established as part of geographical expansion that may have had some resource acquisition as a byproduct rather than a goal. 47 3.2.3 Casting Whether or not bronzes were locally cast at Panlongcheng has also been a longstanding question, as it was also theorized that the raw materials ostensibly sent from Panlongcheng to Zhengzhou were cast, then occasionally sent down to Panlongcheng in the form of completed bronzes to reinforce the power of the elites who relocated there (Liu and Chen 2012: 296). However, even as early as the 1970s, there has been speculation that Panlongcheng had some capacity for local casting based on small finds such as “smelting crucibles” (Hubei Provincial Museum 1976: 51), though the evidence of those remains being used for smelting was somewhat tenuous and were not associated with anything else related to bronze casting (Lu et al. 2020: 67). Further speculation of local casting came from cast-on repairs, both to fix issues that occurred immediately after casting and also to repair defects caused after use. These repairs include recasting of entire vessel sections (e.g., a whole leg), and some of these vessels had been repaired several times, including one jia vessel that had thirteen repairs (Zhang 2014: 63). As Zhang notes, the skills required to repair bronzes are not dissimilar to the skills required to cast entire bronzes. Since it is unrealistic to think that these repairs would have required shipment back to Zhengzhou only to return to Panlongcheng, these repairs suggested that Panlongcheng did have the capacity local casting. Solid confirmation of casting occurred when 6 models were collected from the eastern beach of Xiaozui in 2013, which lead to further investigation that revealed ceramic molds, slag, crucibles, furnace walls, bronze fragments, whetstones, production facility remains such as patina surfaces and earthen platforms, and more (Lu et al. 2020: 68). Two 48 building foundations (F1 and F2) were also found, as were “ash ditches” filled with charcoal that are typically associated with production activities. The workshop was active from the 4th to 6th periods of Panlongcheng, with most activities dating to the 5th period, and was relatively large in scale (over 70m long E-W) (Lu et al. 2020: 69). Its location is at the north end of Xiaozui, facing the east side of what is currently Pokou Lake but would have been land at the time. As such, the casting workshop was approximately 100km west of the Gongchengqu west gate and would have been accessible by land. Based on its location, Lu et al. believe that it was built to be convenient for both management/production and providing products to the ruling class (2020: 71). Finished products would have been able to move through the city from the west gate to the east gate into Lijiazui, the most important burial site of that period, which was also the most prosperous period of Panlongcheng. Products also could move through the north gate of the city walls up to Yangjiawan as well. Other areas of Panlongcheng – Wangjiazui, Yangjiazui and Yangjiawan - have remains that could potentially be indications of more casting workshops, as they have long ash ditches with charcoal chips, potential crucibles and some copper remains (Lu et al. 2020: 71). Currently the evidence is not as robust as the finds from Xiaozui, but because these finds share some commonalities with those from Xiaozui it is a possibility that requires further investigation. The discovery at Xiaozui indicates that Panlongcheng is not simply an outpost but a regional center with production capacities of its own. That said, while fairly large, the 49 production capacity of Xiaozui was likely not large enough to match the number of bronzes found at Panlongcheng, so either more workshops were required, or some bronzes were indeed still imported from the north. Further analysis and comparison of the metallurgy, casting technology and production techniques between Panlongcheng’s workshop and those from Zhengzhou and Yinxu can also help clarify the nature of bronze casting during the Shang and the regional differences in production (if any) between the sites despite the stylistic similarities. One possibility is the analysis of the ceramic molds, which are a crucial part of the bronze casting process. Considering the recent evidence of some metallurgical differences compared to Zhengzhou, analysis of the ceramic mold composition may continue to indicate a distinct technological style at Panlongcheng. 3.2.4 Molds A great deal of attention has been paid to the spectacular bronzes of this period, not simply because of their size and meticulous decoration (though those certainly play a large part) but also because of their unique manufacturing process. Rather than casting bronze sculptures via lost-wax, which was the predominant casting method in Europe and elsewhere, bronze vessels cast via the “piece-mold method” were the prestige items in China during this period. This method applies clay to a carved model, which is then cut into pieces, removed from the model and reassembled before pouring the molten metal. Decoration is carved directly onto the mold pieces before reassembly. The production process of bronzes therefore required extensive knowledge of both metallurgy and clay-50 working, manifesting in what Ursula Franklin (1999) proposed to be a prescriptive, assembly line-esque form of production by the Yinxu period. Digital image analysis of ceramic molds thus has the potential to add another layer of complexity to our understanding of bronze casting during this time. Microstructural research on clay molds has also been fruitful, especially regarding clay preparation practices (Stoltman 2009; Stoltman 2018). However, much of the research on clay molds has been shaped by the sheer abundance of material unearthed at Yinxu, all of which are made with the aeolian loess that dominates the area. Tens of thousands of clay molds have been excavated in Anyang, while the molds are limited to hundreds from Zhengzhou and even fewer from sites outside of the Central Plains. As such, research regarding bronze-casting and craft production in the Bronze Age tends to focus on the benefits of using the loess of the Central Plains for casting. The molds recently unearthed at Xiaozui, however, were likely made from local materials at Panlongcheng where the environment differs from the Central Plains. As such, adaptations to the overall casting process would have been necessary to account for the different mineralogical composition and content in the region. Chapter 7 uses digital image analysis to examine the microstructural composition of these molds, exploring how southern residents adapted bronze casting mold technology to the local environment. The analysis examines molds from Yinxu, Zhengzhou and Panlongcheng, alongside results from the previous two sections, to explore the clay-processing component of the manufacturing sequence. 51 3.2.5 Ceramics While the outstanding bronzes of early Bronze Age China are signifiers of elite ritual and status, its pottery is, as in many other cases, more associated with the daily lives of the average early Bronze Age residents. The pottery of the early Bronze Age is typically described as “utilitarian” due to its abundance and rather unremarkable appearance, particularly in contrast to porcelain that developed in China later. Considering its more quotidian association and less stringent formal qualities, daily-use pottery allows for experimentation and adaptation within certain restrictions (i.e., habitus). As detailed in Hein’s (2016) account of research history, the evolution of Chinese ceramic classification has gone through several phases and has inspired much debate that continues to this day. In general, however, the accepted approach is to first classify ceramics and sherds (when possible) according to vessel type, each of which has a specific name (e.g., li, pen, zun, gang) and use further descriptors in relation to this name (Hein 2016:34). Of each vessel type there will be several different variants, of different colours, regional styles and changes over time. The common li tripod cooking vessel, for example, can be found in different colours, different heights, different rim styles, with thin legs or bulbous bottoms - but they are all considered li (Figure 3). Another example would be the zun (a large wine vessel), with the subclassification da kou zun (large mouthed zun). This approach is likened to a “genealogical tree” of sorts (Hein 2016: 35). A great deal of time and effort has been spent focusing on whole vessels and creating detailed typologies and chronologies based on ceramic form, allowing for relatively swift matching of sherds 52 to these pottery types (Li 2014:139). Discussion throughout this dissertation will refer to various pottery types using these traditional classification names. a) b) Figure 3. Li – a) Panlongcheng vs. b) Yinxu. 3.2.5.1 Assemblage In his analysis of both formal style and assemblage, Sun (2017) argues that the pottery of Panlongcheng reflects the development of a regional style influenced simultaneously by the Central Plains to the north and local cultures surrounding the area. Using a diachronic analysis, Sun concludes: 1) The pottery assemblage reflects replacement of earlier and local forms as the population settles in the area. In the early phases of the area, there is a disappearance of flat-footed tripod vessels and burnished black earthenware pots and urns that were rarely found in the Central Plains, replaced instead by wares mostly found in the Central Plains. Hard pottery and “proto-porcelain”, a type of pottery considered a precursor to more traditional porcelain, also appears during this time. 53 2) Alongside the construction of the city site, the assemblage grows to incorporate more wares associated with the Central Plains alongside more “local” styles like da kou zun, dou, jia and others. Additionally, while the Central Plains “rope” decoration continues, other decorative styles rarely seen in the Central Plains like the chevron and basket pattern are also incorporated. The middle phases of Panlongcheng appear to be a time of cultural change and variation. Li cooking vessels also become more heterogeneous, where earlier forms have been preserved and new regional characteristics gradually develop. 3) The assemblage, like that in the Central Plains, gradually becomes more homogenized and stable in later periods. But the assemblage differs in terms of proportion and decoration. For example, there is a rapid increase of da kou zun, gang, proto-porcelain and stoneware pottery during this phase which were, again, not commonly found in the Central Plains. Increasing variation in both form and decoration of da kou zun also occurs. Meanwhile, the proportion of rope pattern drops significantly (e.g., 40% in phases I and II to 22% in phases IV and V at Wangjiazui, echoed also at other locations), while other local patterns increase (the increase of certain patterns is also due to the increase in proto-porcelain, as these are only found on this type of pottery). Additionally, the signature red and yellow fabric associated with Panlongcheng becomes most obvious in these later periods, in contrast with the grayware of the Central Plains. 54 Sun’s (2017) conclusion is that “the material culture of the people from the Central Plains culture who came south gradually developed their own characteristics due to the geographical barrier, thus differentiating from the culture of the Central Plains who came south later” (64, trans. Sacharuk). While he does not name diaspora or ethnogenesis explicitly, his analysis effectively suggests the development of new cultural practices reflected in the pottery assemblage and decoration. As one would expect with a diaspora, their practices are initially consistent with forms one would find from their place of origin (the “homeland”) and gradually incorporate and develop new styles into their repertoire based on local interactions, experimentation with forms, and an understanding of local materials. Near the later stages of Panlongcheng, these forms become hardened into a consistent regional style that has both signature differences and clear overlap with the Central Plains. While Sun’s analysis focuses on potential influence from local groups, there is currently limited access to ceramic samples from said groups. However, this is not to exclude potential influence from local populations and instead opens up the possibility of both environmental and cultural influence, particularly because local populations would have been familiar with local materials and resources. Sun notes in his analysis, however, that an understanding of technology is needed for further exploration of this topic. This is echoed by Campbell et al. (2022), who question whether current evidence supports the prevailing unidirectional models and emphasize the need for further research regarding ceramic production in Erligang. While we are able to identify sites containing Erligang ceramic types during this period, the details of 55 technology and production are underexplored. This lack of information tends to reify the core-periphery interpretation of the Erligang period by focusing heavily on the distribution of ceramics without taking into consideration local changes, influences, innovations and adaptations of “peripheral” centers over time. As such, Campbell (2014b) argues that the addition of research into ceramic production and sourcing “would go a long way toward making ceramic tradition distributions more meaningful” (125). 3.2.5.2 Stoneware and Protoporcelain As noted by Sun (2017), proto-porcelain and stoneware make an appearance at Panlongcheng even during its earliest phases. Both types are characterized by extremely high firing (1000-1200 degrees Celsius), with style, firing techniques and raw materials differentiating the two. While stoneware was mostly found in the lower Yangtze River during the Erlitou period, it was abundant in the Central Plains by Erligang. Proto-porcelain can also be found in Erlitou, though in small quantities. These high-fired wares are most commonly found in elite contexts indicating their status as prestige goods, and as such their origins and circulation once again have implications for the interactions and political economy of the early Shang (and beyond). There has been ongoing discussion about the origins of these hard wares, as no kiln sites have been found in the North. As such, it was generally believed that these wares originated in the South and were circulated Northward. Newer research analyzing chemical compositions and petrography are questioning this assumption, and some scholars now believe that the relevant technology was developed independently in both 56 regions. For example, an analysis of 21 sherds, including 3 from Panlongcheng, indicate that Northern samples have different quartz grain sizes and distribution patterns compared to Southern samples (Li 2023). Another recent analysis (Zhu et al. 2023) using proton induced X-ray emission (PIXE) to measure the chemical composition of proto-porcelain samples suggests that most samples from Erlitou and Yinxu (n=83 for all samples) did not originate from kiln sites in the lower reaches of the Yangtze (in Zhejiang Province, known for its early and prolific production of porcelain) and were instead manufactured at different, yet unidentified sources. With regard to Panlongcheng, it was previously believed that these wares were imported from the southern reaches of the Yangtze as no production centers had been found. In 2014, researchers sorting through remains from the 4th and 5th layers of the Northern slope of Yangjiawan discovered that the number of high-fired pottery remains was much higher than usual (Xu 2018). In general, the proportion of stoneware unearthed at Panlongcheng is less than 2%, and slightly higher at elite residences (e.g., 2.6% at the northside of the large F4 building foundation in Yangjiawan), but it was 3.2% and 5.9% in the 4th and 5th layers respectively. When excluding abnormally large gang sherds from the count, the proportion jumps to 12.6% and 21.6% respectively, which is similar to the Wucheng site (20.4%) that is also known for its high fired pottery production (Xu 2018: 126). Alongside the high proportion of hard pottery sherds, 15 out of 75 pieces had “bubbling” on the surface, indicating defects. Considering the rate of defects, the rarity of these types of “bubbling” sherds in other areas of Panlongcheng, and the concentration of 57 hard pottery in this area, Xu speculates that this is near a potential hard pottery production site. More densely concentrated stone ware sherds were also found near the lake beach under the Northern slope of Yangjiawan, which may have been more waste products (Xu 2018: 127). No kilns have yet to be found, however. Xu (2018:127-129) identifies three different types of hard pottery found at Panlongcheng that suggest local production rather than imports: 1) Close imitations that retain slight details of Erligang-style pottery. In these cases, it is difficult to discern with the naked eye whether the samples are imitations or imports, but some slight deviations can suggest local production. As an example, Xu notes a hard pottery guan found in Xiaozui that closely replicated the elements of “typical” hard pottery from the lower Yangtze in terms of shape, vessel, decoration and production process but still had slight changes that looked like local Panlongcheng style. Xu suggests that these types of remains “may indicate that the craftsmen who imitated the stoneware were still local residents of Panlongcheng and in the process of making the vessels still unconsciously retained the shape characters of Panlongcheng pottery” (Xu 2018: 128, trans. Sacharuk). 2) Erligang-style shape and decoration using stoneware materials and production techniques. The shapes of imported stoneware from the lower Yangtze typically have different shapes and decorations of ordinary pottery from the Central Plains, so the combination of the two are quite rare and unique. For example, a proto-porcelain pen from Xiaozui is high-fired, sintered, with a blue-gray glaze 58 but little surface decoration, with a shape that is common in both Panlongcheng and Zhengzhou but absent in the lower Yangtze River region. Another example is a stoneware gang, which is a shape strongly associated with Panlongcheng specifically. These sherds have typical da kou gang “thunder” decoration pattern, but with a grayish-white body and a thin glaze rather than the red low-fired characteristics of the common gang. 3) Ordinary pottery that imitates certain characteristics of hard pottery. An example is a muddy grey pottery urn in a lei shape. This shape is generally associated with hard pottery and bronzes, but is otherwise made from everyday materials and is decorated as a common piece of pottery. This echoes the practice of bronze-imitation pottery that appears in the Erligang and continues through the late Shang. Based on these recent finds, Xu (2018) argues that Panlongcheng is a key link connecting the Central Plains and the southern region, and that Panlongcheng hard pottery was directly or indirectly affected by sites from the lower reaches of the Yangtze River and south of Panlongcheng. While these recent finds indicate that Panlongcheng likely had local production, Xu still believes that most of the hard pottery came from these other sites considering the discrepancy in scale and number. Xu also believes that the main sources of hard pottery at Zhengzhou and Panlongcheng are the same due to the high degree of correlation between vessel types. However, considering the recent chemical and petrographic evidence discussed above, it 59 may be the case that Zhengzhou and Panlongcheng had different production centers. If Panlongcheng did indeed supply Zhengzhou with hard pottery, however, it appears as though they were discerning with their selection and sent only the best pieces to Zhengzhou as the quality (thicker glazes and imprinting on stoneware) is higher (Xu 2018: 130-131). Regardless of whether Panlongcheng supplied hard pottery to Zhengzhou, the examples provided by Xu indicate a blending of styles between the Central Plains, Panlongcheng and the lower reaches of the Yangtze, with Panlongcheng’s local craftsmen using newly developed techniques to incorporate their existing stylistic repertoire. With local production capacity being likely, it also indicates the technological capacity, labour and resources dedicated to hard pottery in Panlongcheng. This continues to clarify that Panlongcheng was not only a strong regional center with its own craft production processes, but that the Panlongcheng craftsmen continued to experiment and develop their own identity that blended old forms with techniques and influences from their new environment. 3.2.6 Soils Aside from differences in decorative and formal style, there are also environmental differences between Panlongcheng and the Central Plains that the residents would have adapted to and become accustomed to over generations. These “non-human actors” are parts of a community that have an impact on identity-formation, with Cipolla (2017) and 60 Malouchos (2020) both arguing that these environmental encounters by diasporic populations can be catalysts for ethnogenesis. When it comes to ceramic technology, soils are going to have the largest impact. Soil availability will impact collection practices and organization, workability, firing, functional differences after firing, and more, all of which influence the chaîne opératoire and the habitus of the craftspeople. Soils also reflect large climatic conditions that would have impacted residents as well. Soil resources at Panlongcheng differ greatly from Zhengzhou, as Northern and Southern China - naturally demarcated by the Qin Mountains - have distinct environmental characteristics. Zhengzhou is located in the Central Plains, in Henan Province, and is part of the Loess Plateau where aeolian loess dominates, both wind-blown and water-deposited, which is composed primarily of silt-sized grains (Kerr and Wood 2004:90). Loess is characterized by its grain-size consistency due to ongoing sorting via wind depositing and its relatively low clay content. Current maps also include a layer of luvisols/cinnamon soils which developed alongside aeolian loess, but these regions would have nevertheless still had an abundance of loess in its soil mixture. Current soil maps show fluvo-aquic soils (i.e., alluvial soils) and paddy soils (from water logging) dominating in the Jianghan Plain area in which Panlongcheng (Wuhan) is situated, highlighting the difference between soil resources in these two provinces, though some luvisols/cinnamon soils are also available. Figure 4 highlights the difference in soil availability between the Central Plains (Zhengzhou/Anyang) and Panlongcheng (Wuhan) from a modern perspective to provide a sense of the disparity. Systematic coring across 61 Panlongcheng was undertaken to explore the characteristics of the vertical and horizontal spatial layout of stratigraphic accumulations, including both cultural and natural sediments (Zhang et al. 2018). They found that there are four main types of accumulation: 1) Reticulated laterite, the most common natural sediment. Red or reddish-brown, likely developed from weathered bedrock, more plastic and less fertile than loess (due to higher clay content and acidity), and is >50% silt content; 2) Yellow laterite, also known as “Xiashu Loess”. Mostly yellow or yellow-red silty clay, widely distributed on the edges of hills in the middle and lower reaches of the Yangtze; 3) Brown or “Cinnamon” soil, widely distributed on the highlands of Yangjiawan, Yangjiazui, Louziwan, Jiangjiawan and Xiaozui to a lesser extent. Not necessarily brown in color (black, dark gray, yellow brown and gray-white are also common), with it being clay or silty clay; 4) Stone accumulation. Found during concentrated exploration of Yangjiawan, irregular in shape and 0.3-0.5 meters in diameter. 62 Figure 4. Hubei & Henan 1:1 million soil type map (2018). Anyang (Yinxu), Zhengzhou and Wuhan (Panlongcheng) are outlined in black. The origins of Xiashu Loess have been debated, as it may have formed from the weathering of underlying limestone (Zhu 1988) or from weathering of aeolian deposits like the 63 windblown loess of the Central Plains (Zhao and Yang 1995). Hong et al. (2009) conducted a study of the laterite profile in Jiayu county, Hubei (~105 km south of Panlongcheng) and concluded that it is in fact of a combination of both processes, with the upper soil beds being derived from aeolian accumulation modified by chemical weathering during the Pleistocene, while the lower soil beds originated from in situ weathering of the underlying argillaceous limestone (614). In the Jianghan Plain, where Panlongcheng is situated, erosion and deposition from both the Yangtze and Han rivers have led to the accumulation of alluvial deposits with wide-ranging particle sizes (Sun et al. 2021). A reconstruction of the paleoclimate and depositional environment, via Jianghan Plain river and lake beds, characterized sediments from these contexts at the time of Panlongcheng as clayey sands (Wang et al. 2012). As per their analysis, the area has gone through periodic shifts of aridity and humidity along with changing hydrodynamic conditions. Wang et al. (2012) interpret the early Bronze Age climate as warm and humid, with much vegetation and marine plant life. The sediments, influenced by these environmental conditions, gradually increased in the amount of clay (higher humidity) and sand (higher lake level and more hydrodynamic activity) during and after settlement while silt-size decreased. Zhang et al. (2005) focused attention on the mid-Pleistocene archaeological sections by examining grain-size, silt-to-clay ratio (Kd) and magnetic susceptibility. Since the grain-size tends to be much finer than the aeolian soils in the North, this suggests that the loess had been modified by secondary processes. They point out that the percentage in coarse sand grade decreases from the north to the south, and the percentage of fine sand 64 and clay-size material increases southward (Zhang et al. 2005: 133). The Kd ratio serves as an indicator for climatic changes and also suggests that the clay content increased during periods of higher temperatures and humidity, while magnetic susceptibility suggests stronger pedogenesis due to the higher percentages of ferromagnetic grains (Zhang et al. 2005: 137). Panlongcheng’s soil is indeed known for its high iron-oxide content, leading to much of its pottery being fired red rather than the everyday “grayware” of the North. The coring study also indicated that Panlongcheng residents undertook terraforming projects, as the distribution of cinnamon soil is accumulated in some areas where it is impossible to have developed naturally. Cinnamon soil in the region likely originated from lacustrine and fluvial accumulations due to high water levels around Panlongcheng, which had dropped and exposed these sediments by the time of occupation. For example, there are layers of blue-gray/gray-yellow cinnamon soil on top of hillocks in Louziwan and Jiangjiawan, the coloration suggesting that the soil developed in a reducing environment (i.e., a deep stable lake); the accumulation structure of the cinnamon soil is also inconsistent with its layering in the lake basin, indicating it was manually transported (Zhang et al. 2018). Cinnamon soil was also found on the northern slope of the Yangjiawan hillock. Using monte carlo distribution analysis and two-way histograms, Zhang et al. (2018) demonstrate that the distances between cinnamon soil and the laterite accumulations in Panlongcheng show strong spatial correlation at short distances. It appears as though the Panlongcheng residents manually transported cinnamon soil from nearby lakebeds to the hillocks around Yangjiawan to expand the area, reduce the impact of soil erosion on the 65 hilly areas, and strengthen the stability of the landform (Zhang et al. 2018: 145). On steeper parts of the hill, residents also used stones for levelling and reinforcement, which explains the stone accumulation found in concentrations in Yangjiawan during their coring study. Interestingly, cinnamon soil was also used as part of building foundations in Jiangjiawan when the laterites (being slightly more like the dominant material for building foundations in Zhengzhou and later Yinxu) would be the obvious choice (Zhang et al. 2018: 142). The soil availability in Panlongcheng was therefore marked by both familiarity and unfamiliarity. If the ceramic practice of the relocating residents was like that at Yinxu, clay-y alluvial deposits were available by way of the Yellow River and were used to produce li cooking vessels (Stoltman et al. 2018). As such, soils with higher clay content would not have been unfamiliar to Panlongcheng settlers but were not the primary soil source for most products. Instead, Central Plains pottery and its bronze molds are crafted using loess (Stoltman et al. 2018). While “Xiashu loess” is available in Panlongcheng, complex processes of pedogenesis via chemical weathering decreased grain-size and increased clay content, making it different in terms of workability. That said, these materials seem to have been appealing considering that the city walls and most building foundations appear to use the laterite for their building materials, and the subsequent integration of cinnamon soil into some projects (e.g., terraforming and building foundations) indicate later adaptive approaches using additional, different materials. The following analysis will clarify the difference in raw materials available at Zhengzhou and Panlongcheng and will expand upon what practices were maintained and changed in the face of a new environmental reality which, in light of other recent research regarding Panlongcheng material culture, 66 can continue to highlight the development of a regional identity and the implications that has on interregional interaction during the Erligang period. 3.3 Conclusion The Erligang cultural landscape, as a continuation of that from Erlitou, appears to be a network of sites involving population dispersal originating from the Central Plains interacting with local groups. The hybridized forms of material culture at each site reflect unique interactions, historical trajectories and environmental realities, but further exploration into microstructural and technological aspects of the material culture can provide additional layers and depth of understanding into production and technological style. The recent analyses and discoveries at Panlongcheng have highlighted such internal processes that blend Central Plains material culture (the “Metropolitan” variant as per Campbell (2014a)) with influences from further down the Yangtze River, particularly in terms of hard-fired pottery. Panlongcheng also developed its own terraforming projects during the expansion of the site, as well as its own local bronze-casting industry. These new discoveries, combined with the recent ceramic analyses done by Sun (2017) and Chen and Jing (2020) highlight the ways in which external factors catalyze local practices and, in turn, identity formation. In light of new information on soil differences, firing technology and local bronze casting, I will be exploring the technological underpinnings of ceramic technology from Panlongcheng and Zhengzhou using digital image analysis to quantify grain- and pore-size 67 distribution, shape characteristics and firing temperature. Some recent preliminary work on Panlongcheng ceramic technique and technology has already been done in terms of pottery making techniques, firing temperature and microstructure (Chen and Jing 2020). Chen and Jing observe that while pottery production from the Central Plains gradually shifted from wheel-thrown to hand-formed, Panlongcheng production was more flexible and would combine multiple techniques together. They argue that this indicates a heavily standardized production process in Zhengzhou and its surrounding sites, while the pottery of Panlongcheng adhered less strictly to any one particular production technique. They also briefly describe qualitative observation of scanning electron microscope samples from Panlongcheng and Zhengzhou, observing that Panlongcheng samples tend to have smaller particles and be fired at lower temperatures with some notable exceptions (i.e., stoneware and “protoporcelain”). As with Sun’s work, Chen and Jing touch on the idea that these ceramic differences reflect a regional identity that nevertheless maintains a connection with the Central Plains. My aim in this research is to offer expanded, quantitative understanding of petrographic composition of Panlongcheng and Zhengzhou ceramics, exploring the similarities and differences in technological styles from these two sites. The chapters will establish quantitative SEM image analysis baselines for grain- and void-size, morphology and firing temperature of Panlongcheng and Zhengzhou ceramics of differing types. This will clarify the differences in raw materials and firing practices at each site. The purpose of these chapters is to add quantitative detail to our understanding of ceramic distributions to expand upon the dynamics and interregional interaction present during the Erligang 68 period, and to more thoroughly understand the nuances of Panlongcheng settlement through ceramic technology. I use a framework of diaspora to refocus attention to local experiences and adaptations to a new environment. This research will not only help us better understand the interaction between these two major sites and encourage investigation into the technology of the other Erligang material traits, but will also highlight the importance of exploring ceramic technology in all Erligang satellite sites. 69 Chapter 4: Digital Image Analysis: Principles and Applications Images are an essential part of archaeological research. Archaeological illustration, mapping, photography and microscopy enhance our understanding of the sites and artifacts we analyze and enable us to present nuances and complexities that can be difficult to explain through words alone. However, these images have largely served as descriptive supplements or as subjects to slower, manual analysis. Within the last 20 to 30 years, technological and computational advancements have allowed for more advanced analysis of the images themselves, and just recently has this technology become inexpensive and accessible enough for more widespread adoption. As opposed to a traditional photograph, which uses photosensitive paper to capture wavelengths of light visible to the human eye, a digital image is an array of picture elements (often shortened to pixels or pels) that represent discrete numeric quantities of light intensity or gray levels. These pixels are captured via a camera with a digital sensor that converts light to these numeric quantities that can then be read and represented by a computer. Given these images are, at their core, grids of numbers, they can be manipulated, processed and analyzed through mathematical equations and algorithms. The density of pixels (the image’s resolution) determines the amount of detail in the image and the amount of information stored within each image file. The bigger the grid, the more storage is needed and more processing power is required to apply complex algorithms. As such, while digital image analysis has been understood and used for decades, only with the development of increasingly powerful and inexpensive personal computers has digital image analysis become available to large numbers of researchers. The development and 70 proliferation of open-source technologies and programming languages has hastened the use of digital image analysis even more. That being said, digital image analysis is still less commonly used in archaeology than in other fields, like geology, medicine and materials engineering. Ceramic petrography, which examines microscope images of ceramic thin sections, can benefit greatly from regular implementation of digital analysis techniques. The subfield has traditionally relied on more manual methods like visual estimations (Matson 1970; Rye 1981:50-52) and point-counting (Schubert 1986; Stoltman 1991), which have their advantages, but digital image analysis can be another useful addition to a petrographer’s arsenal. This chapter will detail ways in which petrography can benefit from digital image analysis, particularly using backscattered electron microscope images. This chapter is not meant to serve as a comprehensive guide to digital image analysis, which can instead be found in books such as Burger (2009), Heilbronner and Barrett (2013), Jähne (2002) and Jain (1989). Rather, this chapter provides a basic and non-intimidating introduction to the field of digital image analysis, particularly for archaeologists, and especially those working with ceramic thin sections. The chapter will first provide a brief history and literature review of digital image analysis applications in archaeology, followed by a basic technical overview of digital image acquisition, processing and analysis workflows. It will also elaborate upon the application of digital image analysis for images of ceramic thin sections. This chapter is written in the hopes that readers will be inspired to delve into projects of their own and more fully explore the digital 71 image analysis research that has occurred and is occurring in various subfields of archaeology. 4.1 Digital Image Analysis in Archaeology Archaeology has been quick to adopt digital image technologies in some subfields and slower in others. Mapping has been particularly open to advanced tools like geographic information systems (GIS), satellite imaging, remote sensing and photogrammetry. The vast majority of papers related to archaeology and digital imaging in the 1970s and 1980s were related to mapping in some form or another (e.g., Burnside et al. 1983; Dorsett et al. 1984; Gruen and Baltsavias 1990; O’Brien et al. 1982; Scollar 1978; Stone 1991; Torleàrrd 1986; Wynn 1986). By the 1990s, mapping had extensive research dedicated to digital image analysis, and relatively advanced techniques like feature extraction continue to be discussed and implemented (e.g., Blaschke 2010; Cowley 2012; De Laet et al. 2007, 2009; Vletter 2014). The prevalence and early adoption of digital image analysis in mapping is likely related to the advanced technological requirements (not to mention the baseline level of technical skill) needed to acquire and analyze the maps in the first place. Bioarchaeology has used medical imaging technology in the study of human remains since the X-ray was developed in the 1890s (Beckett and Conlogue 2010). However, the regular application of digital image analysis to bioarchaeological research has some difficulties, as access to medical imaging equipment and personnel can be challenging and expensive, while field-based tools pose significant issues with regard to interpretability (Beckett 2014). The images acquired from certain imaging technology, like 72 MRIs, are also unique formats that require specialized software and knowledge for appropriate analysis. There are also obvious and fundamental differences between ancient samples and hydrated, living bodies, so “attaining quality images using medical imaging instrumentation in the context of mummy science often requires an override of manufacturer imaging presets” (Beckett 2014:137). Quantitative image analyses can also be difficult because of the realities of degradation. Any attempt at paleoimaging should therefore consider the minimum procedural standards outlined by Beckett and Conlogue (2010) and Beckett (2014). The related subfields of geoarchaeology, petrography and ceramics also have long connections to digital image analysis. The Computer Applications & Quantitative Methods in Archaeology (CAA) conference has been held nearly every year since 1973, and early proceedings included the computerized analysis of pottery and lithic shapes (CAA Online Proceedings). That said, the analyses were on digitized drawings, rather than images captured by technologies with digital sensors (be they cameras or microscopes), and an early paper on the automatic capture of shapes by “television camera” notes the prohibitively expensive nature of image processing that had stalled the progress of the field (Wilcock and Coombes 1985). Predictably, adoption of digital image analysis started to pick up in the mid-1990s as personal computers became more readily available. In 1995, Durham et al. extracted shape information from images of artifacts and compared a number of different algorithms for classification using the data acquired, and an early application of neural networks in the analysis of use-wear images appeared in 1996 (Barceló, Vila, and Gibaja 1996). 73 More recently there have been great advances in the use of both digital image analysis and neural networks for petrographic research. For example, Dal Sasso et al. (2014) use digital image analysis to quantify textural and mineralogical characteristics of pottery from Mesolithic and Neolithic Al Khiday (Sudan). Their analyses, using SEM images and elemental maps, identified changes in paste recipes and production technology over time. They identified an average decrease in quartz temper grain size from the Mesolithic to the Neolithic and a decrease in temper variability over this same time frame. The inclusion of quartz temper can help aid pottery’s thermal shock resistance, although extremely large temper particles can impact ceramic strength (Tite et al. 2001: 308-309). They postulate the change in quartz size and variability is due to an increasing level of standardization for cooking ware because of the increased consumption of cereals (which require longer cooking times) during the Neolithic, balancing both strength and thermal shock resistance (Dal Sasso et al. 2014: 137). Aprile, Castellano, and Eramo (2014) combined image analysis with neural networks (colloquially this is usually and inaccurately referred to as artificial intelligence or “AI”) in an attempt to automatically classify mineral inclusions and pores using images acquired from potsherd thin sections. They first segmented three classes (quartz, calcareous aggregates and pores), extracted the features and labelled the samples to build a training set for the classification model. Using ANNs (Artificial Neural Networks) and a modular classification architecture, they were able to automatically classify calcareous aggregates and quartz on cross polarized light digital images with 94.3% and 95.7% accuracy, respectively. They plan to apply this methodology to more quickly 74 automate the identification of other mineral inclusions, further apply the work to distinguish between naturally occurring and manually added tempers, distinguish between pottery fabrics automatically and/or validate petrographic observations. Other studies have attempted to automate classification based on texture measures (Hein et al. 2018), match sherds to vessels (Blanco-González et al. 2014), characterize porosity (Reedy et al. 2017), and study ceramic paints (Josa et al. 2009). Another study semi-automated textural analysis of ceramic thin sections, measuring size, shape characteristics and thickness of aplastic inclusions (Reedy et al. 2014). Such analyses can help aid in the statistical comparison of technological practices and quantify technological variation in ceramic production. Archaeological research has already benefited greatly from the application of digital image analysis across numerous subfields. Now that digital image analysis tools have never been more accessible, this analytical method has the potential to contribute a great deal more. With affordable personal computers, open-source software and robust scientific communities collaborating on new image analysis techniques and tools, researchers can begin to apply digital image analysis to research projects relatively quickly. The following sections detail the basic foundational information necessary to understand and begin a research project, beginning with the ways in which computer vision and human vision differ and complement each other. 75 4.2 Digital Image Analysis versus Human Vision The use of digital image analysis is not simply to make existing analyses quicker, but to complement the strengths of human vision with the separate strengths of computer “vision”. What is fundamentally important to note in the comparison between human vision and computer vision is the way in which humans contextualize the environment when assessing an object or an image. The surrounding environment may make certain objects appear bigger or smaller, or certain colours appear brighter or darker, depending on the surrounding environment and/or adjacent objects. Optical illusions are easy illustrations of this process, such as the Bisection Illusion (Figure 5) or the Müller-Lyer Illusion (Figure 6). The Bisection Illusion makes the vertical line appear longer than the horizontal line, despite being the same length, and the Müller-Lyer Illusion makes identical lines look like different lengths due to the different orientations of the arrowheads (Muller-Lyer 1889; Wade 2017). Figure 5. Bisection illusion. The lines are the same length despite the vertical line appearing longer. 76 Figure 6. Müller-Lyer Illusion. The lines in the center are identical but appear different due to the direction of the arrowheads. Individual perception and interpretation are also issues. Human vision is the result of a number of transformations of light - the cornea and lens focus light onto our retina, which is part of our central nervous system, and that in turn sends information from its photoreceptors to our optic nerve. The optic nerve then transforms the information it has received into a cortical representation of the image. While the process is more or less the same across humans, individuals are highly varied and may not see the same image in the same way. A recent, popular illustration of this is “the dress” internet phenomenon, where some people see a dress as blue/black and others as white/gold. Researchers suggest that assumptions about the illumination of the dress (i.e., whether or not the dress was under natural or artificial light) strongly influenced the subjective interpretation of the colour (Wallisch 2017). While humans are prone to errors and bias, human vision is particularly well-suited to identifying objects and patterns. With the Kanizsa Triangle (Figure 7), for example, one is immediately able to identify two different triangles despite the fact that these triangles do not fully exist in the image. Thus, humans are able to “fill in the gaps” and interpolate 77 objects even when presented with few visual details and identify and recognize objects even when they are partially occluded. Figure 7. Kanizsa Triangle. Humans are able to fill in gaps and see shapes or patterns despite missing or occluded visual information. Computer vision’s strength, in contrast, is objectively assessing measurements like length and area. This is because of the aforementioned structure of digital images, which are simply numeric matrices. Computers are not “seeing” lengths but can instead be programmed to calculate them. This fact also helps circumvent differences in display quality, as computer monitors are not consistently produced or adjusted. Thus, even if two individuals were to have identical vision, their monitors may output different gray or colour levels that could influence their interpretation of an image or may have different resolutions that change the image’s display. Differences across monitors is therefore less relevant in digital image analysis, which relies primarily on the underlying matrices that comprise an image, although having a high-resolution monitor of any kind is beneficial for any human-based analysis and assessment described later. Similarly, computers are less equipped to identify patterns and objects as quickly as human beings, although computer scientists are actively working to improve upon this by using machine learning algorithms and massive datasets to train models for 78 identification. While there have been huge advances in this field in a short amount of time, the models still have a long way to go until they are able to identify patterns and objects as quickly and with as much confidence as a human being. Digital image analysis is not meant to replace human analysis but is instead a way to harness the strengths of both computer and human vision. Semi-automated quantitative analysis of images can help avoid bias or error that is inherent in human vision and can speed up data collection that is often time-consuming because of a need to mitigate these problems. Human analysis and judgment, however, are still required to choose appropriate algorithms for the image and the research question, and to iterate upon image processing steps to ensure that the data collected fits the patterning on the image. The next section will elaborate upon the minimum steps required from image acquisition to analysis, and how both human and computer vision are necessary to complete this process. 4.3 Digital Image Acquisition In digital imaging, images are acquired via electrical signals converted into numeric quantities that can be read by a computer. Numerous technologies are available to do this now - digital cameras (nearly ubiquitous now in smartphones), magnetic resonance imaging (MRIs), digital microscopes, digital x-ray machines, and so forth. A sensor is used to first collect the electrical signals, while a frame-grabber is used to convert these electrical signals into discrete numeric quantities interpreted as an image by a computer. 79 Having a workable computer for basic digital image analysis can be easily achieved these days. Jähne (2002) identifies four key requirements for a workstation: 1) a high-resolution image display, 2) sufficient memory transfer bandwidth, 3) sufficient storage space and 4) sufficient computing power. At the time of Jähne’s publication, nearly two decades ago, he noted that general-purpose computers already had sufficient memory (RAM), storage capacities, processors and monitors to work on these tasks. Since then, the average computer has gotten even more powerful. That said, the requirements for digital image analysis will vary based on the number and size of the images, the software being used and the algorithms being employed. Minimum hardware requirements for the researcher’s chosen software can and should be researched, particularly when analyzing an especially large dataset with high-resolution image files. 4.4 Digital Image Composition The size of an image, assuming a rectangular shape, depends on its width and its height. For digital images, the width M corresponds to the number of columns, and the height N corresponds to the number of rows. The values at each intersection of a column and a row is a picture element - commonly known as a pixel or a pel, which represents the intensity or brightness of light at that spot, and each pixel corresponds to coordinates on the matrix. The coordinates start at (0,0) and end at (M-1, N-1). Unlike typical graph conventions, where the y-coordinate moves from top to bottom, the first value (0,0) originates at the top left of the matrix. This value corresponds to the top left of an image (Figure 8). While this 80 article focuses on the analysis of 2D images, 3D images are simply those with a third array of values (i.e., a third dimension). Figure 8. Image segment (left) and underlying pixel values (right). The higher the density of pixels, the higher the resolution of the image. If an image is low-resolution, it will be difficult to discern the details of an image (Figure 9). On the other hand, extremely high-resolution images will result in larger file sizes and longer processing times. The necessary resolution for one’s purposes will depend on one’s research question and the information needed to be extracted from an image. A resolution can also be converted to real-world measurements, such as dots per inch (DPI) or pixels per inch (PPI). These conversions are particularly important when the analyses require information about specific lengths (e.g., the size of a tumor, size distribution of grains). 81 Figure 9. Low-resolution vs. high resolution. A grayscale image consists of a single channel with values representing light intensity or brightness. The range of values depends on the number of bits per pixel, a bit being the smallest unit of computer data, where k = the number of bits per pixel and the range = [0… 2k-1]. Only positive integers are used since negative values of light intensity are impossible. A binary image, which consists only of black and white, has a range of 0 (black) to 1 (white). A typical grayscale image has a range of 0 (black) to 255 (white), where k = 8 bits (1 byte). In cases where this range is still too macroscopic, such as in medical imaging, the number of bits/pixel can increase even more - up to 16 bits (and thus a range from 0-65535). Colour images can also be analyzed but require more processing power, as they require three channels of values rather than one. The basic structure of a digital image is simply a matrix, which can then be manipulated, enhanced, and analyzed using a whole breadth of equations and algorithms. However, the quality of an image and the desired outputs are important factors when conducting automated quantitative analysis. The following section will elaborate upon key 82 considerations when one is working through a typical digital image analysis workflow of image acquisition, image segmentation, image and data analysis. 4.5 Digital Image Analysis Process Digital image processing and analysis can be fairly straightforward or extremely complex, depending on one’s goals. A basic workflow however consists of three to four main steps: 1) Image Acquisition, 2a) Image Segmentation, 2b) Image Processing and Artefact Removal, and 3) Data Processing and Analysis. Segmentation, where the objects of interest are separated from the rest of the image, is at the heart of digital image analysis. Steps 1 and 2a are ultimately meant to facilitate segmentation, and the subsequent analysis and data processing is derived from the data acquired from this crucial step. 4.5.1 Image Acquisition While digital images are matrices of numbers, they can be saved in a variety of formats that compress and store the information differently. For scientific purposes, Tagged Image File Format (TIFF) files are extremely common because of the format’s flexibility, as TIFF files can “store a number of variations of an image in different sizes and representations together” in a single file (Burger and Burge 2009: 14). Formats will differ based on the tools used and the task at hand. Mapping images, for example, are often stored as Scalable Vector Graphics or SVGs, which is a type of file format used to store vector graphics which use continuous coordinates rather than discrete coordinates. For a review of different image format types and their respective advantages/disadvantages, see Jähne (2002). 83 Most image acquisition technology will come with its own image capturing software. When acquiring an image, one needs to consider the scope of the area being analyzed. It is important to acquire the largest field of view while still retaining the details needed for analysis. For example, if one wishes to quantify the number of particles in a microscopic thin section, one will want the magnification on the lowest setting that allows for robust sampling while also retaining visible shapes of the particles. This ensures the ability to segment them from the background without data loss. It may be necessary to acquire images at different magnifications depending on your research goals. One should also want to ensure that the image is in focus, homogeneously illuminated (i.e., the sample is not tilted) and properly contrasted. A blurry image is difficult to fix, will appear darker and will cause objects outside of the focal plane to look larger than normal. Heterogeneous illumination will cause similar objects to appear different; consistent staging is essential for this step and will depend on the image acquisition technology you are using. Finally, the background and foreground should have sharp contrast to facilitate segmentation later on. One must be careful not to overly contrast the image; it is fairly trivial to enhance contrast slightly using processing tools, but it is difficult to undo excessive contrast. Having consistency across all three of these qualities is key. Fixed settings during image acquisition (e.g., automated focusing, a stable sample stage set at the same distance) can help with this; use of a static calibration object to tune settings to at the start of any acquisition session is also an option. A final consideration is the conversion of pixels to real-life measurements. Most image-capture software automatically includes a scale, but if this is not the case then a 84 physical scale should be included, on the same stage as the object in question, in every image. 4.5.2 Open-Source Image Analysis Software The following steps (Segmentation, Image Processing) will require some sort of image processing and analysis program, whether pre-built or coded in a suitable programming language. Along with rapid advancements in technology, free and open-source image analysis solutions are now readily available. There are a number of benefits to using open-source software and programming languages for analysis. Waber (2020), in his research on open-source tools for lithic gesture analysis, argues that “financial inaccessibility is directly contrary to the principles of the scientific method, which is predicated on the replicability of the methods and results of one’s study”, and that “lowering the financial barriers to participation in research also diversifies the community of researchers and enables (and invites) the presentations of more perspectives, opinions, and ideas, ultimately enriching archaeological narratives” (6). It is for this reason that I not only recommend, but encourage, adoption of open-source tools for the implementation of image analysis for archaeological research. In their review of image analysis packages, Reedy and Kamboj (2003) advocate for the use of Image Pro-Plus over the open-source software ImageJ. However, Image Pro-Plus is approximately $5500 USD, whereas ImageJ, Python and R are completely free. While Image Pro-Plus may have certain features that facilitate analysis, the price tag may pose a hindrance to 85 departments, lab students and/or researchers who simply cannot afford the cost of a license. Open-source tools also tend to be cross-platform (Windows, MacOS, Linux) and relatively lightweight, meaning specific and advanced hardware is not an issue (which can be another prohibitive cost). Comparisons of ImageJ, Python and R by Pennekamp and Schtickzelle (2013) found that R tends to be the most resource-heavy, with Python and ImageJ completing image processing tasks relatively quickly and easily. ImageJ also does not require specialized programming knowledge, so its learning curve is relatively low compared to Python. As such, for researchers interested in digital image analysis alone, ImageJ is a free, straightforward and powerful program that will continue to be maintained so long as the community wishes this to be the case. For researchers who are interested in more advanced image analysis approaches that incorporate machine learning, it may be beneficial to take the time and learn Python. The workflow detailed below can be accomplished with any image analysis software, and the segmentation algorithms described can be implemented via Python and ImageJ through their robust library/plugin selections. 4.5.3 Image Segmentation In order to properly analyze the objects in question, they need to be segmented from the rest of the image. The subsequent image, with only the desired objects, is called a feature image. 86 There are a number of different options and algorithms that can be used to isolate objects from the background of an image. The most basic approach is pixel-based thresholding, where values above a certain threshold are converted to white (1) and values below a certain threshold are converted to black (0) (Figure 10). The easiest way to identify the threshold is to plot the values on a histogram; ideally the histogram will be bimodal and the threshold will fall in between the two peaks. If the edges are symmetrical, the ideal threshold will be the mean gray value between these two peaks. Other algorithms have been developed to optimize the threshold beyond using the mean, such as Otsu’s method that minimizes intra-class variance and/or maximizes inter-class variance (Otsu 1975). Figure 10. Otsu’s Thresholding. https://scikit-image.org/docs/dev/auto_examples/segmentation/plot_thresholding.html. Accessed August 7, 2020. While the pixel-based thresholding approach is quick and simple, it does run into problems when images have uneven backgrounds or when there are numerous objects with varying gray levels in the image. Even with homogeneous illumination across the image, segmentation in this way will lead to darker objects becoming too small and bright objects becoming too large because the gradual changes of gray values at the edges will be thresholded up or down accordingly, and it can only be corrected if all objects are the 87 same gray value. Though beyond the scope of this dissertation, other alternative approaches to segmentation are edge-based region-based and watershed segmentation. 4.5.4 Image Processing and Artefact Removal Despite the plethora of options, one may find consistent issues in segmentation regardless of the chosen method. This is likely due to issues with the image itself - not enough contrast, heterogeneous light, and so on. In an ideal world, image acquisition processes would mitigate these issues perfectly but this is not necessarily realistic. Luckily, minor problems in image quality can be corrected. An important tool for this purpose is normalization, which changes the range of pixel values so that each image being analyzed has a similar data distribution (usually in the form of a gaussian curve). Normalizing image data helps issues like poor contrast, and also helps make every image being analyzed more consistent. Another option is to apply changes based on regions of pixels, otherwise known as filters. A median filter, for example, assesses the median gray value of a pixel and its neighbours, and then replaces the pixel value with that median. This can help remove noise in the image that may impact segmentation while also preserving the boundaries of the objects. A gaussian filter, by contrast, replaces a pixel’s value with one that makes sense within a gaussian distribution of it and its neighbours’ values. The size of the filter is called a kernel, which by default is typically a square 3x3 pixels. The filter is applied across the image until every pixel has been appropriately converted based on the filter’s algorithm. 88 Even after relatively successful segmentation, one might notice isolated foreground pixels that the segmentation methods erroneously left as objects (called “artefacts”). If these isolated pixels are small enough, they can usually be removed easily with filters or “erosion” processes in which objects of a size smaller than a certain threshold are converted to background values (usually black). Relatedly, there may be small holes within larger objects that need to be filled - this can be done through dilation processes that fill isolated background values with gray values similar to the pixels’ neighbours. Like segmentation, combinations of processing techniques may be needed. Additionally, one must remove objects that are obscured/bisected by the image border. This can be done fairly trivially using Python’s scikit-image function clear_border(), or “Exclude on Edges” from ImageJ’s “Analyze Particles” tool. 4.5.5 Data Processing Once the image has been segmented, one needs to verify that the feature image is accurate. Manual point-counting of a random selection of segmented images is one option, external verification (e.g., comparing the quantification of grain phases by also quantifying the respective EDS colour maps of SEM images) is another possibility, and qualitative comparison between the segmented and original image can also work. Once the accuracy of the segmentation has been verified, the underlying image data can then be used to answer different research questions. One may quantify the number of particles in an image (or the percentage of particles in an image), the average area of the object, or the length and height of an object 89 in question. Shape metrics include aspect ratio (minimum diameter/maximum diameter), roundness (4piA/P), compactness (sqrt((4/pi)*area)/major axis) and form factor (4pi*area/sqr(perimeter)). Most of this can be done with ImageJ plugins or Python libraries. With ImageJ, once these values are calculated, the data need to be exported in tabular format (.csv, Excel) that can be used for statistical analysis in SPSS, Python, R or any other preferred analysis software. 4.6 Digital Image Analysis and Thin-Section Petrography Thin-section petrography is a geological technique used to identify minerals that has been employed in archaeological research since the 1930s (Shephard 1936). It is particularly useful in ceramic analysis, which are effectively human-made rocks composed of both naturally occurring and intentionally added minerals and rock fragments. It can be used to characterize ceramic textures, source raw materials and examine pore structures. Considering the ubiquity and importance of pottery in the everyday life of ancient societies, understanding the microstructural composition of ceramics can help archaeologists identify trade routes and exchange patterns, ceramic production patterns, technological style, ceramic function and even demographics (Blanco-González et al. 2014; Fitzpatrick, Dickinson, and Clark 2003; Parsons 2012; Stoltman 1991; Stoltman et al. 2005; Stoltman et al. 2009). For example, microstructure can help match ceramic sherds to regionally unique raw materials sources which can then be used to identify trade routes and distribution (Fitzpatrick, Dickinson, and Clark 2003; Stoltman 1999; Stoltman 2005). Additionally, due to the habitus of ceramic production, looking at microstructure to identify 90 specific manufacturing techniques or raw materials preferences may indicate demographic shifts (i.e., the arrival of new groups in the region) (Parsons 2012). The technique requires the careful preparation of a thin-section. A slice of a sherd (typically at a right angle to the vessel wall) is mounted to a 27mm x 46mm glass petrographic slide, polished and then attached using an epoxy. The sample is then cut again to make a “thick section”, further ground down to a standard thickness of 30 μm (1 μm = 1 micron = 0.001 millimetres) and covered with glass (Stoltman 2001: 9). Thin-sections are often analyzed using a polarized light microscopy or a scanning electron microscope (SEM). Unlike an optical microscope, which uses visible light and a fixed stage, a polarized light microscope filters the light into waves vibrating in only a single plane and often features a rotating stage. A second polarizer is added to filter light in a plane perpendicular to the first (i.e., “cross-polarized light”). By doing so, one can observe identifying characteristics of each mineral by their effects on the light. The use of cross-polarized light allows for the identification of minerals via colour (although all opaque minerals will appear dark, as light is unable to pass through them), relative refractive index, cleavage and fracture tendencies and more. Size and shape characteristics can also be identified. For grain-size analysis, using a polarized light microscope requires either visual estimation, based on comparison charts, or the point-counting method, where an analyst records the minerals that appear under a crosshair (on the eyepiece of the microscope) at fixed intervals. The visual estimation method is quick but prone to subjectivity, while the point-counting method is accurate and precise but relatively slow, as point-counting 91 requires at least 200 points per slide for proper sampling (Stoltman 2001: 13). An additional drawback to the polarized microscope is its magnification limits due to its reliance on the wavelengths of visible light. In scanning electron microscopy, atoms of a particular sample are struck with a concentrated bundle of electrons (an electron beam), and the atoms are subsequently reflected or backscattered out of the material to produce a grayscale image that includes compositional and topographic information (Krinsley et al. 1998: viii). If the sample is made into a thin section prior to analysis, an enlarged image, along with the “identification of mineral constituents, their relative abundances [and] associations and states of alteration” is possible (Rapp 2009: 25). Backscattered Electron (BSE) imaging is particularly effective for mineral identification because of its excellent resolution and contrast, and the high magnification possible. When an accelerated electron beam is aimed at a target, elastic and inelastic collisions occur between the target and the electrons from the beam. With elastic collisions, the electrons scatter at different trajectories depending on the target, and larger atoms result in more collisions due to their larger area. Gray levels are therefore a function of mean atomic number, with darker areas having lower atomic numbers. Quartz will appear extremely dark, silicates like plagiocase feldspars are brighter, and Fe-Ti oxides or rare-earth minerals are extremely bright. Using BSE thus allows for a quantitative determination of the relative abundance of minerals, as well as quantification of grain size and form (Krinsley et al. 1998: 26). BSE combined with image analysis is also well-suited to the analysis of porosity and is commonly used in materials science for this purpose (e.g., 92 Edwin et al. 2019; Lyu et al. 2019; Wong, Head, and Buenfeld 2006). While thin-sections need to be impregnated with a dyed epoxy resin when studying porosity under a polarized microscope to better distinguish particles from pores, this is not the case with BSE. Energy dispersive X-ray Spectroscopy (EDS), meanwhile, measures the unique atomic structure of each element, presenting them as a unique set of peaks on an X-ray spectrum. EDS can convey more information than BSE, as EDS allows for compositional mapping with colour rather than grayscale alone. EDS, however, cannot detect elements with molecular weights below that of carbon and cannot identify particular bond structures, and is thus only used for the detection of inorganic compounds. The use of BSE in conjunction with EDS “provides a powerful tool for mineral identification, porosity determination, and study of fine-scale grain-to-grain and intra-grain relationships”, and is useful for differentiating between mineral phases like quartz, feldspars and clays (Krinsley 1998: 52). The inherent qualities of BSE - relative gray values, high contrast - make BSE images particularly suited to digital image analysis, and EDS mapping can be used in conjunction as an external verification or additional layer of the analysis workflow. Point-counting can also be used on a random sampling of BSE images by overlaying a rectangular grid at 1mm intervals in order to verify the accuracy of the image analysis process. The implementation of digital image analysis with thin-section petrography is not necessarily meant to replace point-counting, which remains an important and reliable technique for analysis. Point-counting is particularly useful when characterizing smaller samples or samples with no prior contextual information, samples that have similar 93 looking features of similar atomic numbers, and samples with features of very small particle sizes. Digital image analysis, in contrast, is suitable for larger datasets with variable objects of interest, particularly those that are larger in size (Livingood and Cordell 2009: 871). It serves as an additional technique that can build robust datasets of extracted information, quantify shape and size metrics, and the methods used for analysis can easily be shared and reproduced using open-source software macros and/or code. The following chapters implement digital image analysis to extract size and morphological information of particles and voids, as well as aggregating proportions per image (particles, voids, matrix), to get a sense of the quantifiable differences between northern and southern ceramic technology. Building on the point-counting research done by Stoltman et al. (2019) on Yinxu ceramics, the following chapters show how both size and morphological characteristics can be used to statistically assess technological differences between sites and the changes that may occur when groups encounter new environments.94 Chapter 5: Panlongcheng and Zhengzhou Grain-Size Distribution and Morphology 5.1 Grain Size and Morphology Grain size is an important descriptive property of siliclastic sedimentary rocks that reflects sedimentation processes. Particle sizes are dependent on, and potentially reflective of weathering, erosion and transportation mechanisms and depositional conditions. Thus, if materials are sourced from different sedimentary environments, particle size distribution can be used for differentiation of said materials. Since such soils and clays are used for ancient ceramics, similar analyses can be used to characterize the technological systems and choices involved in ceramic craft production, so long as local environmental contexts are considered, as different regions may have similar sedimentation processes. As a reminder (see Chapter 3.2.6), the predominant material at Zhengzhou is aeolian loess, an accumulation of yellow silt-heavy material primarily blown in from nearby arid regions in Northern China and Mongolia (like the Gobi Desert). Due to ongoing sorting via wind-blown deposition, loess is characterized by its grain-size consistency and its relatively low clay content. By contrast, Panlongcheng has an accumulation of clayey and sandy alluvial and lacustrine deposits from the Yangtze and Han Rivers, as well as reticulated and yellow laterites that underwent in situ weathering. According to Zhang et al. (2015) smaller grain sizes in this region compared to the Central Plains suggest secondary weathering processes. We would therefore expect Zhengzhou materials to have larger and more consistent grain-size distributions than Panlongcheng, and less angularity due to the consistency of wind-blown weathering. 95 Visualizing and assessing grain-size distribution (GSD) has been discussed at length since the 1960s (cf. Friedman 1967, 1979; Vandenberghe 1975; Visher 1969). There are a few factors that make the analysis of GSD a relatively complex pursuit. First is the range of sedimentary rock size, which extends from megaliths, measured in kilometers, to clays, measured in fractions of millimeters, or microns (μm). Due to such an extensive range, it is impossible to fully capture the entirety of a sample regardless of sieve-size or image resolution. Thus, mathematical summaries like mean, standard deviation, skewness and kurtosis require adjustment. Additionally, because particles vary in both shape and density, it can be difficult to succinctly and accurately represent a grain-size distribution. The diameters of particles are ultimately estimations and, in the case of thin section analysis, there is a sectioning bias that needs to be accounted for if one requires analysis of exact measurements (as opposed to, in this case, relative difference) (Friedman 1958). As such, analyzing and comparing grain-size distribution requires multiple graphical and mathematical representations. Shape characteristics like circularity, roundness, solidity and aspect ratio can be used to further characterize particles, which may reflect erosion, transportation, deposition, manual alteration (e.g., crushing) and composition. The values for the first three measures range from 0 to 1. Circularity is a measure of a particle’s similarity to a circle, with 1 being a perfect circle, and is used to characterize the particle shape. Roundness, as defined by ImageJ, is a measure of circularity irrespective of the sharpness of edges; i.e., a pentagon approximating a perfect circle will have a roundness value of 1 whereas an elongated rectangle will have a lower roundness value. This is not to 96 be confused with roundness as a textural descriptor – i.e., irregularity of particle edges. Solidity instead is the best measurement for irregularity, defined as its total area divided by its convex hull area (put simply, the area the particle would take up if an elastic band were wrapped around it). For solidity, a value of 1 = perfectly smooth. Solidity values can tell us whether particles are irregular, but they cannot tell us the source of that irregularity – that is, the shape may be naturally occurring or manually caused. Visual observation and assessment of each image, as well as an understanding of the local soils, is still necessary for such distinctions. Aspect ratio measures the elongation of a particle (major axis/minor axis), which can represent different particle types. Characterizing particle shape using axes measurements would ideally take both elongation and flatness (depth axis/major axis) into consideration. Unfortunately, given SEM-BSE images are two-dimensional, this is not possible. Aspect ratio can, however, offer at least some idea of the particle shape, differentiating for example extremely elongated minerals like mica to more compact particles (relatively speaking) like quartz. 5.2 Digital Image Analysis of Panlongcheng and Zhengzhou Samples The SEM images used for this project present some challenges. First, there are multiple foreground objects with varying grayscale values; histograms are often multimodal rather than bimodal and therefore cannot be thresholded in a binary way without losing key information. Multimodal thresholding algorithms (e.g., multi-otsu thresholding) do exist, but such methods are further complicated by the existence of aggregate particles that 97 partially share similar grayscale values with non-related objects. As a result, removing certain objects based on grayscale values alone, rather than their relationship to surrounding particles, will lead to aggregate minerals being partially (and incorrectly) segmented. Additionally, the images have numerous objects that are touching. Clay particles, individually too small to identify even in an SEM, appear as amorphous blobs, often surrounding various minerals. Simply thresholding above a certain value will therefore lead to undersegmentation, where objects that should be separated appear as a single object. To address these issues, this project uses the Trainable Weka Segmentation (TWS) plug-in for the popular open-source image processing software ImageJ (Fiji distribution). This plug-in combines an interactive graphical user interface (GUI) with machine learning algorithms in the data mining and machine learning toolkit Waikato Environment for Knowledge Analysis (WEKA). Pixels from the image data are classified as belonging to a certain segment manually by the user, who interacts with the image directly via the GUI. These pixels build up a training dataset for the machine learning algorithm, which then classifies the remaining pixels. Users are able to see the classification results quickly with a new/old image overlay that compares classified pixels with the original image, and they are then able to retrain the classifier with updated data if the algorithm pixels appear misclassified based on the new/old image comparison. Figure 11 shows an example of the overlay, where a different color corresponds to each class. One can “Toggle Overlay” to view the original image. In this example, some of the matrix is dark green which indicates 98 that those sections have been misclassified as quartz – the user can manually classify some of those pixels as clay matrix and retrain the classifier to improve accuracy. By default, TWS uses a Fast Random Forest algorithm to determine the likelihood of classes. Random Forest is a supervised machine learning algorithm that classifies data (pixels, for this project) using multiple decision trees via image features (edges, textures and noise) and inputs (in this case, pixels input and classified by the user). Decision trees use the training data (e.g., image features and inputs) to determine the likelihood of an input being placed into a particular class by splitting into different nodes when the data show divergence. The Random Forest algorithm takes the mode output (a class) of the multiple decision trees for each input. The Fast Random Forest algorithm implemented in TWS is optimized for speed and memory use, making the training and classification process extremely fast. TWS does allow for the selection of other algorithms instead of Fast Random Forest, but this project uses the default due to the demonstrated accuracy of Random Forests for pixel-based classification in other research (Lormand et al. 2018; Mahapatra 2014). 99 Figure 11. WEKA trainable segmentation overlay, where the colors of the labels are overlaid atop the original image. This helps the user visually assess the accuracy of the model (and continue to improve the classification with additional data, if necessary). TWS avoids the segmentation issues identified above by 1) allowing for multiple classes/segments (up to 5) and 2) using a wide range of image features via different image filters to identify edges, textures, noise and membranes-like structures. In other words, TWS looks at the pixels through different lenses rather than just grayscale values alone and is therefore able to classify based on more extensive information. 5.2.1 Methodology A total of 26 samples and 54 images were acquired (Error! Reference source not found., see Appendix A for all images). The Panlongcheng ceramics were primarily excavated in 2014 as part of excavation work in Yangjiawan around the large-scale building foundation 100 F4 by the School of History of Wuhan University (see Chen 2018). Most samples (11/16) are from Periods 3-4, four are from Periods 5-6, and one final sample (likely a Pen) from M26 in Yangjiazui is from Period 2. Contextual information for the Zhengzhou samples is limited. All images are the same resolution (2048 x 2176) and magnification (500x, 537 μm working distance) to ensure consistency. The magnification of 500x was chosen as it was the best field of view to incorporate as many grain sizes as possible (Fine to Coarse Silt) without compromising quality. Images were only used if taken in a central part of the sample (i.e., sample edges were excluded). Following Stoltman (2019) and Chen (2020), Shang ceramic paste can be broadly categorized into those with high frequencies of sand-sized inclusions (“tempered”), those with low frequencies of sand-sized inclusions (“untempered”), and those with highly refined kaolin clay (“protoporcelain”). Of the samples, 10 were untempered of various types (guan, pen, gui, da kou zun, zeng) (Panlongcheng n=6, Zhengzhou n=4), 8 were tempered gang (Panlongcheng n=5, Zhengzhou n=3) and 8 were tempered li (Panlongcheng n=5, Zhengzhou n=3). Refer back to Figure 3 for images of li, and see Figure 12 for examples of untempered vessels and Figure 13 for gang. Since gang are primarily found in Panlongcheng and considered a Southern style, they are analyzed separately from li (despite both being tempered wares) to determine whether Zhengzhou-excavated gang are imports. While the number of samples is relatively limited, especially for Zhengzhou, the data points acquired for each image were both numerous (~100-300 silt grains) and detailed versus typical point-counting analysis (see Error! Reference source not found.). Thus, for a preliminary analysis, these images should be sufficient to discern overall 101 technological difference between the sites during the middle periods and illustrate the need for more samples from all relevant early Bronze Age sites. Type Site # of Samples # of Images # of Silt (12.5-63 μm) Grains Median # of Silt Grains per Image Gang Panlongcheng 5 10 3015 272.5 Gang Zhengzhou 3 7 1791 259.0 Li Panlongcheng 5 10 2166 201.0 Li Zhengzhou 3 6 361 62.5 Untempered (da kou zun, guan, pen, zeng) Panlongcheng 6 13 3507 281.0 Untempered (guan, gui, pen) Zhengzhou 4 8 1750 213.0 26 54 12590 236.0 Table 1. Number of Samples and Image Analyzed. 102 a) b) c) d) e) Figure 12. Untempered ceramic assemblage a) guan b) pen c) zeng d) zun e) proto-porcelain lid (Wang et al. 2016) Figure 13. Gang with various bases. (Wang et al. 2016) 103 5.2.2 Image Pre-processing For each image, the scale was set (382 pixels per 100 μm, as per the scale attached to the image) and was then removed via an automated script (also known as a macro) to ensure consistency cropping. Slight qualitative adjustments were made to contrast and brightness (Image -> Adjust -> Brightness/Contrast) on some occasions, but for the most part the different phases were well-defined and did not require adjustment. Some images were slightly noisy (small specks of brightness variation) so a denoising filter was applied (Image -> Process -> Noise -> ROF Denoise -> Theta 15.00). 5.2.3 Training and Segmentation The image was segmented using the Trainable WEKA Segmentation (TWS) package, a pixel-based classifier that uses a Fast Random Forest algorithm to distinguish up to five classes. Prior to analyzing the images in question, the classifier was trained on 25 images of similar but unrelated content (i.e., samples from different sites) in order to ensure equal classification strength for all images analyzed. Five classes were identified - voids, three different grain types (aligning with BSE differences in quartz/feldspar, Fe-Mg, and Fe-Ti oxides), and clay matrix. The voids are all black (0) and one of the grain types is all white (255), and the rest of the classes are different grayscale values. Grain class 1 is approximately 148-192, grain class 2 is approximately 208-238, and matrix values overlapped across all grains. That said, grayscale values varied across images - the relative difference between the grains was more salient to the training process. Note that the grain classes were separated during 104 training so as to not confuse the training data, but were ultimately recombined into the same image for analysis. For each image, approximately 10 sections of ~40-50 pixels for each class were selected and the classifier was retrained. The TWS interface overlays its segmentation estimations after training. If estimations appeared incorrect, more pixel training was added - this process was continued iteratively until segmentation was deemed successful on a qualitative basis by comparing the newly generated overlay with the original image. The “Get Probability” option was then selected, which produces a stack of images for each class. This stack of images was saved each time for reference. The separate images for grain classes 1, 2 and 3 were added together (Process -> Image Calculator), resulting in an image ready for thresholding (Image -> Adjust -> Threshold -> Otsu). After thresholding, the image was despeckled (Process -> Noise -> Despeckle). This is a median filter that replaces pixels with the median of its 3x3 pixel neighborhood, removing isolated pixels. This process removes stray pixels that may affect edges. The resulting thresholded image was then compared with the original; any remaining errors were corrected manually. For example, grains that were erroneously split into two due to a crack were rejoined (Figure 14). Figure 14. Closing a cracked particle. 105 Once at a suitable level, desired measurements were set (Analyze -> Set Measurements - Area, Shape descriptors, Perimeter, Fit ellipse, Feret’s diameter) and the particles analyzed (Analyze -> Analyze Particles). After testing with the earlier training samples, a minimum area of 15 μm2 was set. Anything smaller ran the risk of being artifacts of the thresholding process rather than actual particles. Particles on edges were excluded, as their true size was indeterminate. The resulting measurements were saved to a spreadsheet with the sample name, image name, excavation site and ceramic type. 5.2.4 Results - Grains 5.2.4.1 Cumulative Frequency Cumulative frequency graphs are common graphical treatments of grain-size data. Particle diameter (in Krumbein phi units, calculated as − 𝑙𝑜𝑔2𝐹𝑒𝑟𝑒𝑡1000) is typically placed on the x-axis and cumulative weight percentage on the y-axis. In this case, since weight is not relevant, cumulative area percentage is used on the y-axis instead. A running total of the area taken up by successive particle size classes is plotted up to 100%. Cumulative frequency graphs allow for a quick assessment of data normality (a straight line will indicate a log-normal distribution, and an S-shaped curve for a normal distribution), sorting (gentle and steep slopes for poor and good sorting, respectively), and broad comparisons between samples. Untempered samples were analyzed first to characterize the grain-size distribution of local soils without inclusions. To ensure consistency across types, the particle Feret diameter range was limited to 12.5 to 63 μm (inclusive) – sand grains were excluded and analyzed separately as these can be either natural inclusions or deliberate 106 additions and thus complicate comparisons. Figure 15a-d present an untempered sample from each site, the segmentation of the particles alone for each. The untempered samples closely align with their respective sites (Figure 16), and when aggregated (Figure 17), the cumulative frequency graph indicates that the difference between Zhengzhou (ZZ) and Panlongcheng (PLC) grain-size distribution (as indicated by different slopes) differs most between 3-6 phi (15-125 μm). Zhengzhou’s samples are more heavily dominated by coarse silt grains (>25%) compared to Panlongcheng (<=16%). The steeper slopes for Panlongcheng from [4,5)-[5,6) indicate a mode for these samples for medium silt grains, where Zhengzhou’s consistent slope across two size classes indicates a less sorted distribution. (a) (b) 107 (c) (d) Figure 15. Guan SEM images and isolations. a) Zhengzhou guan sample b) Panlongcheng guan sample c) Zhengzhou guan sample - isolated particles d) Panlongcheng guan sample - isolated particles. Figure 16. Cumulative frequency of untempered samples. 108 Figure 17. Cumulative Frequency of Untempered Samples by Site. 109 (a) (b) (c) (d) Figure 18. Gang SEM images and isolations. a) Zhengzhou gang b) Panlongcheng gang c) Zhengzhou gang – isolated particles d) Panlongcheng gang – isolated particles. Figure 18a-d show the images and segmentation results for one gang sample from each site for illustration. When plotting the gang, the distributions closely follow Panlongcheng’s untempered distributions (Figure 19). Nearly all samples hover around 110 16% for coarse silt and increase sharply to 50-70% for medium silt grain sizes, even those found at Zhengzhou. This is consistent with the view that gang are a typical Southern ware, created and produced in Panlongcheng. Figure 19. Cumulative frequency of gang samples. The images and segmentation results for one li sample from each site are seen in Figure 20a-d. For li, most samples fall in the same general area as other samples from the same origin site (Figure 21). One exception (UBC06-17), originating in Panlongcheng, appears to align more closely with Zhengzhou’s other li distributions. When comparing all types across sites, the sites cluster together across types (primarily in terms of the higher coarse silt percentage for Zhengzhou samples), with the exception of the Zhengzhou gang aligning more closely to Panlongcheng samples (Figure 22). 111 (a) (b) (c) (d) Figure 20. Li SEM images and isolations. a) Zhengzhou li b) Panlongcheng li c) Zhengzhou li - isolated particles d) Panlongcheng li - isolated particles. Zhengzhou li are clay-heavy, resulting in few segmented grains. Panlongcheng li are still dominated by small silt particles. 112 Figure 21. Cumulative frequency of li samples. Panlongcheng li samples cluster together with one exception. Zhengzhou li samples all have higher frequencies of larger silt particles than Panlongcheng li. Figure 22. Cumulative frequency of grain size by site and type. Zhengzhou li and untempered are similar but the Zhengzhou gang is closer in frequency to Panlongcheng types. 113 5.2.4.2 Relative Density While cumulative frequency graphs are useful, the nuances of size distributions can sometimes be difficult to interpret. In particular, the density of particles within a sample is less obvious. In such cases, barplots and histograms can be more straightforward. In order to reflect density, the area taken up by each individual grain was divided by the total area of each image (286,872 μm2, which excludes the scale at the bottom of the image) and then the total area of each grain class (Coarse silt: 31≥x<63 μm, Medium silt: 15≥x<31 μm, Fine silt: 12.5≥x<15 μm) was summed. Note that these results are specific to the image size used and that percentages will vary based on magnification and image size. However, based on preliminary tests, the relative difference between sites should still be consistent so long as the particles captured are within the field of view. Figure 23. Total area percentage of each grain class per image - untempered. Most Zhengzhou samples have large proportions of coarse silt. 114 Figure 23 illustrates the image area percentage taken up by silt particles, which is then subdivided (by colour) into Coarse Silt, Medium Silt and Fine Silt. Zhengzhou images are approximately 11-15% silt, and generally about half of that is taken up by coarse silt grains (5-8% of image area) while medium silt accounts for another 30-50% of the silt content (4-7% of image area). Panlongcheng images by contrast contain less silt overall (6-15% of image area, with 10/13 images under 12%). Medium silt accounts for around 50-60% of the silt content (3-11% of image area), and coarse silt is only about 14-30% of the silt content (0.5-4% of image area). Taking the average area percentage across images, the primary difference between the two sites is that the amount of coarse silt present is about 3.5 times as much in Zhengzhou samples which results in a higher silt percentage overall (Figure 24). Figure 24. Average image area percentage of each grain class by site – untempered. Zhengzhou samples have more silt overall, and 3.5 times more coarse silt in terms of the area of the image. 115 When looking more closely at grain size distributions between 15-63 μm (as the Fine Silt class shows comparatively little difference), Panlongcheng samples have a positively skewed distribution concentrated around 15-20 μm that drop off sharply at around 35-40 μm (Figure 25). For Zhengzhou, two of the samples show consistent distribution across all grain sizes. The other two, UBC06-10 and UBC06-11, do have positive skewness but less so than the Panlongcheng samples. Rather than seeing a drop off at 40μm, all Zhengzhou samples contain some percentage of coarse silt at every interval up to 63 μm, where only one Panlongcheng sample contains some silt above 50μm. Figure 25. Average image area percentage of grain size per sample - untempered. Zhengzhou samples have larger quantities of grains above 50 μm while Panlongcheng samples are more positively skewed. Gang results echo the Panlongcheng patterning (Figure 26). Apart from one sample (UBC06-33 - a thick-walled gang called “hou tai gang”), the results cluster around 2.5-4% 116 coarse silt. In most cases, there is a relatively limited amount of silt grains in general relative to the total image area (13 out of 17 images under 12%). Figure 26. Total area percentage of each grain class per image - gang. Gang samples from Zhengzhou have similar quantities and proportions of silt to Panlongcheng samples unlike other ceramic types. These results also suggest that the gang unearthed in Zhengzhou were made in Panlongcheng and transported north. The average amount of silt content across Zhengzhou images was about 8.5%, with medium silt being 5% of the image area and ~2% for coarse silt (Figure 27). This is in line with the average composition seen for Panlongcheng untempered samples (0.5-4% coarse silt, 3-11% medium silt). Moreover, the positive skewness associated with Panlongcheng untempered samples is also present for gang samples at both sites, again with the highest percentages generally falling under 40 μm (Figure 28). Unlike Panlongcheng untempered samples however, gang samples do appear to have a small amount of coarse material above 55 μm. Since gang are tempered, 117 this may indicate a selection of slightly coarser material or some larger silt grains that end up incorporated when temper is added. Figure 27. Average image area percentage of each grain class by site - gang. This time, Panlongcheng samples have slightly higher quantities of coarse silt but both are in line with the average composition seen in Panlongcheng untempered samples. This suggests Panlongcheng origin of gang found in Zhengzhou. The li cooking vessels differ insofar as Zhengzhou samples contain far fewer silt particles across all grain classes relative to Panlongcheng, whose results continue to be consistent with the untempered samples (Figure 29). The total amount of silt for Panlongcheng adds up to around 10-12%, while the Zhengzhou li range from 1-6% silt. Coarse silt grains for Zhengzhou samples take up less than 2% of the image area for five out of six images, as is the case with medium silt. The relatively sparse presence of silt for Zhengzhou li is also evident when looking at the average image area percentage by site (Figure 30) as well as grain size by sample (Figure 31). 118 Figure 28. Average image area percentage of grain size per sample - gang. All samples have positively-skewed distributions with small quantities of grains >50 μm. Figure 29. Total area percentage of each grain class per image - li. Panlongcheng li have far higher quantities of silt compared to Zhengzhou li across all grain classes. 119 Figure 30. Average image area percentage of each grain class by site - li. Silt takes up 2-3x more area in Panlongcheng li samples compared to Zhengzhou li samples. Figure 31. Average image area percentage of grain size per sample - li. The sparse quantity of silt in Zhengzhou li is evident while Panlongcheng li still have high quantities of medium silt. 120 Stoltman et al.’s (2009:184) analysis of Yinxu li concluded that their material sources were likely alluvial, aligning with local alluvial sources in terms of higher matrix and sand content and lower amounts of silt. These results suggest that Zhengzhou li were made in a similar fashion. Interestingly, UBC06-17 has a relatively similar distribution to Zhengzhou li versus the other Panlongcheng samples and could potentially indicate an import. From the analyses above, Panlongcheng samples have almost less than half the quantity of silt content of the Zhengzhou samples, with the vast majority of its silt in the medium-sized grain class. When we compare the mean amount of Fine, Medium and Coarse Silt present in the images of each type, split by site, the silt contents are similar for all types at Panlongcheng but vary greatly by type at Zhengzhou (Figure 32). The black error bars are included to highlight the range across all images. Figure 32. Average image area percentage of silt grain class by site and type. The amount of silt across all grain classes is similar for Panlongcheng types but differ at Zhengzhou. 121 The Zhengzhou li have low amounts of silt across all classes, while the untempered Zhengzhou samples have high quantities of coarse and medium silt. Panlongcheng ceramics have similar quantities of coarse, medium and fine silt classes across all ceramic types. While Zhengzhou li have low silt content they are high in sand and clay, as can be observed in Figure 20a. As such, it appears as though Zhengzhou potters sought out different (likely alluvial, as per Stoltman 2019) sources specifically for li cooking vessels and used loess for others, while the Panlongcheng residents used similar clay sources for all ceramics. The gang results for Zhengzhou also align with the Panlongcheng gang results far more than the either the untempered or li samples from Zhengzhou, suggesting that the gang excavated at Zhengzhou were likely imported from Panlongcheng or that raw materials sources were specifically sought out to align with Panlongcheng gang clays. There also appears to be some difference in the number and size of sand grains present in each sample. However, only a median of 2.5 sand particles per image were captured in the analysis due to the field of view and further analyses at lower magnifications are required (Table 2). From this limited sample, Zhengzhou li samples appear to contain more fine sand than any other type, and Zhengzhou untempered samples contain more fine and very fine sand than those at Panlongcheng (Figure 33). These results continue to align with the higher sand content found in Stoltman et al.’s (2009) analysis of Yinxu li, which were sourced from local alluvium rather than loess. 122 Type Site # of Sand (>63 μm) Grains Median # of Grains per Image Gang Panlongcheng 26 3.0 Gang Zhengzhou 8 1.5 Li Panlongcheng 16 2.0 Li Zhengzhou 14 2.0 Untempered Panlongcheng 3 1.00 Untempered Zhengzhou 24 4.00 Total 92 2.50 Table 2. Counts of Sand Grains Analyzed. Figure 33. Average image area percentage of sand grain class by site and type. The sample size is limited but suggests differences in sand across sites, with high quantities of fine sand for Zhengzhou li in particular. Note that these results are based on sand visible within the field of view. 123 5.2.4.3 Statistical Moments As noted, it is difficult to properly calculate a true mean of a sample as some grains will inevitably be excluded (due to sieve size or magnification/resolution limitations). However, a mean (and other statistical measures) can be estimated either graphically (using calculations from a cumulative frequency graph) or via “method of moments” developed by Krumbein and Pettijohn (1938). Due to the computational complexity of the latter, its usage has only become more common recently due to the availability of modern computers, particularly in the geosciences (e.g., Poppe et al. 2004; Sathasivam et al. 2015). The calculations for this approach, which were adapted in R for this analysis, are as follows (adapted from Boggs (2012: 53)): Mean (1st moment) ?̅? = ∑ 𝑓𝑚𝑛 Standard Deviation (2nd moment) 𝜎∅ = √∑ 𝑓(𝑚 − ?̅?∅)2100 Skewness (3rd moment) 𝑆𝑘∅ =∑ 𝑓(𝑚 − ?̅?∅)3100𝜎∅3 Kurtosis (4th moment) 𝐾∅ =∑ 𝑓(𝑚 − ?̅?∅)4100𝜎∅4 Where f = area percent (frequency) in each grain-size present m = midpoint of each grain-size grade in μm n = total number in sample; 100 when f is in percent The measurements below (Table 3) were calculated for particles between 12.5 and 63 μm (see Error! Reference source not found. again for sample sizes) and the results s124 ummarize what is seen in Figure 23 through Figure 33 above. Panlongcheng silt is smaller, less varied and more positively skewed (due to clustering around 15-20 μm). The mean silt sizes are fairly consistent within each site - for Zhengzhou, the untempered samples have a mean silt size of 33.36μm and 36.39μm for li. For Panlongcheng, the untempered samples have a mean silt size of 26.66μm, 28.42μm for gang and 28.19μm for li. The Zhengzhou gang have a mean silt size of 26.96μm, aligning more with the Panlongcheng samples than the Zhengzhou samples. Thus, while Zhengzhou’s li materials are likely from local alluvial sources and contain relatively little silt, the silt it does contain is still, on average, larger than that from Panlongcheng. Type Site Mean Std. Dev Skewness Kurtosis Gang Panlongcheng 28.42 1.07 8.69 300.89 Gang Zhengzhou 26.96 1.03 12.06 382.84 Li Panlongcheng 28.19 0.97 8.48 330.27 Li Zhengzhou 36.39 1.35 0.46 177.92 Untempered Panlongcheng 26.66 0.93 8.34 293.87 Untempered Zhengzhou 33.36 1.26 4.13 213.76 Table 3. Statistical Moments - Feret Diameter (μm) of Silt Grains (15-63 μm). The standard deviation is also higher for Zhengzhou samples (1.26μm for untempered samples and 1.35μm for li), reflecting the large presence of both medium and coarse silt, while the standard deviation for Panlongcheng untempered samples is 0.93μm, and 1.03μm for gang. Again, the gang from Zhengzhou align closely with Panlongcheng’s, with a standard deviation of 1.07μm. 125 Finally, the skewness values for the Panlongcheng samples are all above 8, while the Zhengzhou results are 0.46 (li) and 4.13 (untempered). The only exception, again, are the gang samples unearthed at Zhengzhou (12.06). For all metrics, the gang at Zhengzhou align with Panlongcheng gang samples rather than other Zhengzhou ceramic types which reinforce the idea that gang were in fact produced in Panlongcheng and transported to Zhengzhou. 5.3.4.4 Morphological Indicators - Logistic Regression As mentioned above, particle morphologies can be influenced by grain size, transportation type, duration of transport and other processes. In the case of Zhengzhou and Panlongcheng, their respective soil sources are weathered in distinctive ways through transportation - the loess of Zhengzhou is wind-blown while Panlongcheng’s soil sources are water-based. As noted by Boggs (2012:59), experimental studies have indicated that wind is 100-1000x more effective at rounding edges compared to water. Solidity, as a measure of particle irregularity, is the best measure of this smoothing process. However, this is not a measurement of angularity - thus a triangular particle with smooth corners would still have a fairly high solidity value despite being an angular particle. Firing temperature can also have an impact on solidity, as high firing and the sintering process can smooth particle edges as individual grains become less distinguishable from the matrix and each other. 126 Sphericity measures (Roundness and Circularity as measured by ImageJ) are typically dependent on original grain shape, but can be “modified somewhat by abrasion and breakage during transport” (Boggs 2012: 58). Sphericity alone cannot be used to identify a depositional environment however. Aspect ratio too will be representative of original grain shapes (e.g., higher aspect ratios for longer particles like mica). Since I am comparing just two sites, I use multivariate logistic regression to model the difference between particle size and shape characteristics based on proposed origin. The models below focus on grains between 15-63 μm, as this size range will be the most straightforward to compare with all other ceramic types – as with the analyses above, anything larger may require additional analysis due to possible temper, and the fine silt class grains are too small at this magnification to confidently assess shape characteristics. A model analyzing the relationship between Feret diameter and Site is compared with a model using Feret, Solidity, Circularity, Roundness and Aspect Ratio altogether. The logistic regression model using Feret alone indicates a significant difference between the two sites (p<0.01, AIC=5133), with the odds ratio of the site being Zhengzhou equaling 1.04 (Std. Error 0.016) for every one unit of Feret diameter increase. However, the difference between the null deviance (5242.9) and residual deviance (5129) is fairly small, indicating that the model could be much improved. Visualizing the model with the predicted probabilities shows the likelihood of a grain being from Zhengzhou (y-axis) grows with Feret (x-axis) (Figure 34). 127 Figure 34. Predicted probabilities for untempered samples - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter. When running the second model, all variables are significant except Aspect Ratio. After removing Aspect Ratio, all variables remain significant (Feret p<0.01, Solidity <0.01, Roundness<0.01, Circularity p<0.01) and the model has improved over Feret alone (AIC=4893, null deviance = 5242.9, residual deviance = 4881). Holding all other factors constant, the odds ratio of Zhengzhou equals 1.02 (Std. Error 0.017) with every one unit increase of Feret diameter and 1.06 (Std. Error 0.034) with every one unit increase of Solidity. For Circularity, the odds ratio of Zhengzhou is 0.95 (Std. Error 0.016) for every one unit increase, and 1.019 (Std. Error 0.007) for every one unit increase in Roundness. That is, it becomes more likely that a particle is from Zhengzhou when Feret, Solidity and Roundness increase and Circularity decreases (Figure 35). Note that calculation of predicted probabilities control the remainder of the variables using their respective means; since the dataset is strongly unbalanced toward Panlongcheng (i.e., nearly twice as many 128 grains as Zhengzhou, see Error! Reference source not found.), the probabilities on the x-axis of predicting Zhengzhou will skew lower than modeling each variable alone. Gang ceramics don’t appear to have an analogue in Zhengzhou. However, since their grain size distributions and morphologies appear to align fairly closely with those of Panlongcheng’s untempered vessels, I present a logistic regression model comparing Zhengzhou untempered samples and Panlongcheng gang samples. The results are similar to those above. The logistic regression model using Feret alone indicates a significant difference between the two sites (p<0.01, AIC=5038.9), with the odds ratio of the site being Zhengzhou equaling 1.03 (Std. Error 0.014) for every one unit of Feret diameter increase. The difference between the null deviance (5477.4) and residual deviance (5399.5) is again fairly small, indicating that the model could be much improved (Figure 36). Figure 35. Predicted probabilities for untempered samples - site ~ feret + solidity + roundness + circularity. Grains are more likely to be from Zhengzhou if they are larger, rounder and less angular. 129 Figure 36. Predicted probabilities for Panlongcheng gang/Zhengzhou untempered samples - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter. For the second model, again all variables are significant except Aspect Ratio. After removing Aspect Ratio, all variables remain significant (Feret p<0.01, Solidity <0.01, Roundness<0.01, Circularity p<0.01) and the model has improved over Feret alone (AIC=5038.9, null deviance = 5477.4, residual deviance = 5028.9). Holding all other factors constant, the odds ratio of Zhengzhou equals 1.01 (Std. Error 0.017) with every one unit increase of Feret diameter and 1.03 (Std. Error 0.025) with every one unit increase of Solidity. For Circularity, the odds ratio of Zhengzhou is 0.94 (Std. Error 0.02) for every one unit increase, and 1.02 (Std. Error 0.008) for every one unit increase in Roundness. Just like the model above, it becomes more likely that a particle is from Zhengzhou when Feret, Solidity and Roundness increase and Circularity decreases (Figure 37). 130 Figure 37. Predicted probabilities for Panlongcheng gang/Zhengzhou untempered samples - site ~ feret + solidity + roundness + circularity. Grains are again more likely to be from Zhengzhou if they are larger, rounder and less angular. Finally, for li, the logistic regression model for Feret indicates a significant difference between the two sites (p<0.01, AIC=5133), with the odds ratio of the site being Zhengzhou equalling 1.04 (Std error 0.016) for every one unit of Feret diameter increase (Figure 38). Once again, the difference between the null deviance and the residual deviance is relatively small (5242.9 vs 5129.0) meaning there is room for improvement. However, no models using the shape descriptors meaningfully improved the Feret-alone model. This was also tested by excluding the UBC06-17 sample, which based on grain morphology could have been a Zhengzhou sample, but the results are similar. These current results suggest that the best site predictor for li grains is Feret alone. More Zhengzhou samples, however, would be beneficial to validate this conclusion as the grains present in Zhengzhou li samples are relatively limited. 131 Figure 38. Predicted probabilities for li - site ~ feret. Grains are more likely to be from Zhengzhou as they grow in feret diameter, but to a lesser degree than other types. More data are needed since Zhengzhou samples have so few grains. Ultimately these models generally confirm what was visually represented above; Zhengzhou untempered samples differ from Panlongcheng untempered and gang samples via Feret, Solidity, Circularity and Roundness, whereas Zhengzhou and Panlongcheng li differ significantly only in size. Since Zhengzhou li are most likely sourced from alluvial sources, similarities in morphology are not surprising. However, the predictive strength using individual grains is relatively low. While beyond the scope of this current paper, one may consider using multiple grains or even entire images (using machine learning algorithms like Convolutional Neural Networks) if one’s aim is site prediction from SEM images. 132 5.2.5 Voids Some similar approaches are taken to examine void size distribution and shape, which can reflect grain size, grain packing and firing temperature (Rice 1987: 350-351). Rather than examining the size of the pores only, this analysis also considers Geodesic Diameter (the largest geodesic distance of a region, where geodesic distance is the shortest path joining two points within a region), as performance characteristics are more related to “effective porosity” or “apparent porosity”, a measure of the interconnected pore volume relative to the bulk volume than just total pore volume (Tucker 2001:63). Open and interconnected pores are influenced by grain-packing, variations in manufacturing technique, the escape of water and gases during firing and cracks that occur during firing (Rice 1987:350). Further analysis on firing temperature, including analysis of both interconnected and closed pores, is presented in the next chapter. Since the focus in this analysis is on effective porosity, the technological implications are related to characteristics like permeability, a trait that is not related to the presence of closed pores (Rice 1987: 352) and heating effectiveness, which itself is highly related to permeability (Schiffer 1990). If effective porosity differs, one may speculate that the aforementioned performance characteristics will differ as well. These differences may have been mitigated by further adjustments (e.g., surface treatments, wall thickness, firing temperature) but nevertheless reflect technological divergence that may have had effects that impacted subsequent steps in the chaîne opératoire with regard to manufacturing techniques. 133 5.2.5.1 Cumulative Frequency Graphs Figure 39 plots the cumulative area taken up by pores of certain lengths (Geodesic Diameter). Due to limitations of the field of view, only pores >50 μm2 in area were analyzed (see Table 4 for counts). When comparing the untempered samples, the cumulative frequency graph shows that the majority of pores for both sites are relatively short (<100 μm), but the smaller voids (<50 μm) take up a slightly higher concentration of area for Panlongcheng. The gang samples align closely with each other across sites, and also align closely with li samples from Panlongcheng up until 250 μm, where the li have some longer pores. Zhengzhou’s li (n=289) are different from the rest - there are far fewer small pores proportionally compared to the other curves, and a more even distribution of pores above 250 μm. Figure 39. Cumulative frequency of void length by site and type. 134 This graph indicates that the untempered samples across both have similar effective porosity, with only short interconnected pores, while the tempered samples are differentiated by site. Both gang and li at Panlongcheng have similar porosity curves overall, while li at Zhengzhou has proportionally more longer, interconnected pores than any of the other sample types. Type Site # of Voids Median # of Voids per Image Gang Panlongcheng 515 51.0 Gang Zhengzhou 256 42.0 Li Panlongcheng 635 73.0 Li Zhengzhou 289 50.5 Untempered Panlongcheng 760 61.0 Untempered Zhengzhou 1196 159.0 Total 3651 56.0 Table 4. Counts of Voids (>50μm2) Analyzed. 5.2.5.2 Barplot The density of voids was calculated in the same way as the density of particles; the area of each diameter group was summed and divided by the total area of the image. When looking at density in aggregate by Site and Type, these percentages are averaged across the samples. Panlongcheng untempered samples have less than half the average area percentage for each of the size groups (Figure 40). The error bars represent the range across images; while there is some overlap, voids in Zhengzhou samples are consistently longer across all size groups. While the cumulative frequency of the voids is similar, 135 Panlongcheng untempered samples are less dominated by pores than Zhengzhou samples. Considering Panlongcheng samples have lower area percentages for both grains (silt and sand) and voids, this also means that the remainder, matrix, is higher on average for Panlongcheng samples. For li, Zhengzhou samples have a higher total void area, and fairly consistent distribution in area for most void lengths (Figure 41). Panlongcheng samples on the other hand are more dominated by shorter voids, with the exception of voids in the 350-400 μm range for one image. Figure 40. Average image area percentage of void length - untempered samples. Zhengzhou samples tend to have more voids than Panlongcheng samples. 136 Figure 41. Average image area percentage of void length - li samples. Zhengzhou samples tend to have longer voids, likely due to clay shrinking away from large sand grains. 5.2.5.3 Statistical Moments These calculations again reflect the difference in effective porosity between Panlongcheng and Zhengzhou ceramic types (Table 5). As noted above, effective porosity is influenced by a number of factors including particle size, manufacturing techniques, grain packing and firing conditions. We have seen that there are differences in particle size, but firing temperature is also likely a factor (see next chapter). Panlongcheng untempered samples have slightly shorter pores on average, but the Zhengzhou and Panlongcheng results are relatively similar overall. Panlongcheng gang and Zhengzhou gang are extremely similar, further solidifying the consistency of gang technology and indicating that gang were imported from the south. One can see the more drastic difference between Zhengzhou li and Panlongcheng’s li samples. Voids are nearly 60 μm longer on average for Zhengzhou 137 samples, with much lower skewness. Overall, the visual and statistical representations of void size indicate a difference in porosity for the samples across sites, especially when it comes to li cooking vessels. Type Site Mean Std. Dev Skewness Kurtosis Gang Panlongcheng 119.52 10.36 7.57 186.11 Gang Zhengzhou 123.17 10.58 7.63 185.43 Li Panlongcheng 131.90 12.09 11.73 297.94 Li Zhengzhou 186.08 12.04 3.38 184.01 Untempered Panlongcheng 51.97 3.47 18.42 635.53 Untempered Zhengzhou 57.54 4.11 20.02 730.05 Table 5. Statistical Moments - Void Geodesic Diameter (μm). 5.3 Discussion These results further expand our knowledge about ceramic technology in the early Bronze Age and develop our understanding of local ceramic production at Panlongcheng. The first step was to establish grain-size distributions for common ceramics from both sites for baseline comparison, and to investigate grain- and void-morphologies for further understanding of technological differences and potential choices reflected in these measurements. The results first indicate that the raw materials at Panlongcheng differ from those at Zhengzhou. Untempered ceramics at Panlongcheng have almost less than half the silt content, predominantly of medium silt rather than coarse, and its relative silt composition is remarkably consistent across samples regardless of ceramic type. Thus, unlike the Zhengzhou ceramicists, who had access to and chose starkly different raw materials 138 sources for their li versus other types, Panlongcheng residents chose largely similar raw materials across types. Moreover, the silt grain morphology is different between sites for at least the untempered and gang vessels, where the grains at Panlongcheng tend to be more irregular and less spherical (with the exception of actually circular particles) than those at Zhengzhou. The differences in silt grain morphology are likely due to the further pedogenic weathering of laterite in the Jianghan Plain. The sand content is also much lower across the board for Panlongcheng samples, at least for the sand classes captured at this magnification. In summary, the composition of raw materials at Panlongcheng were significantly different across several factors compared to that of their origin region. While there are fewer morphological differences between li silt grains at both sites, the grain distributions still differ. The silt content of Zhengzhou li is far lower compared to Panlongcheng samples across all size classes, the clay content is much higher in Zhengzhou li, and, while data are limited, it does appear as though the sand content differs as well. Where both Panlongcheng and Zhengzhou li have coarse grain sands too large to fit into the field of view, Panlongcheng wares, both gang and li, also have regular additions of very fine and fine sand grains. The variation in temper suggests a functional difference, an environmental difference, or perhaps both. The divergence in li technology continues with large void size. Zhengzhou li have larger and more interconnected pores in comparison to those at Panlongcheng. These voids are likely a product of the difference in sand grains present - Zhengzhou has plentiful amounts of large coarse sand grains, where Panlongcheng has fewer (but even larger) grains added. With larger and more plentiful interconnected pores, the Zhengzhou li would 139 have been more permeable and more prone to heat loss without mitigation efforts (like higher firing temperature, discussed in Chapter 6). In turn, Panlongcheng li would lose water less readily and would be potentially better for retaining heat. This is dependent on several other factors such as vessel size, firing temperature, wall size and/or surface treatment, that require experimental efforts for clarity, but the difference in porosity likely would have affected performance or necessitated adjustments, resulting in changes in technological style either way. Lastly, the research strongly indicates that the gang samples found in Zhengzhou were made in Panlongcheng. The gang samples unearthed in Zhengzhou match the grain-size distributions of gang samples from Panlongcheng, and the void size distributions closely match as well. Additionally, running a logistic regression model comparing the gang to Zhengzhou untempered paste composition shows that the gang, like those from Panlongcheng, tend to have smaller, more angular particles due to the differences in weathering processes between Panlongcheng and Zhengzhou (alluvial and chemical weathering versus wind-blown, respectively). 5.4 Conclusion As was argued in Chapter 2, differences in technological style can reflect the “active nature identity-making” and the on-going process of developing social boundaries (Stark 1998). However, since this process will often be gradual – due to the persistence of habitus, embodied learning and the close relationship between practice and identity – “hybrid” forms can arise when old and new traits and processes blend to form something 140 uniquely localized. The results above thus reflect several different forms and stages of this process that one might see in a diasporic population, whose identity is shaped both by their cultural history and its ongoing transformation. At the most basic, we see adaptation to local environments in the creation of common ceramics while maintaining their overall form. Those migrating from the north to Panlongcheng would have had to adjust to the material differences, adapting their non-tempered ceramics to more clayey materials with smaller grain size, and the workability of materials would have most certainly been impacted by the increase in clay matrix. Greater amounts of clay increase the amount of plasticity, but also shrink a great deal after drying, so adaptation from silt-heavy, less plastic loess would be required. Thus, while soil difference seems relatively straightforward and, perhaps, even simplistic as a change, these results reflect a blended ceramic style that maintains its form while the chaîne opératoire of its creation was required to adapt. This is an example of a “non-human actor”, as referenced by Cipolla (2017) and Malouchos (2020), shaping and transforming the activities of a freshly settled community into something new. The process of knowledge acquisition requires time, effort and experimentation and can be a transformative experience. While settling communities may initially attempt to apply their techniques to new environments, they will gradually change their methods and habits as they become more familiar with their surroundings and the idiosyncrasies of newly encountered resources (Rockman 2013). Thus, while we see recognizable ceramic forms from the north, the petrographic information indicates a disruption in the chaîne opératoire that would have shifted the technological style of 141 ceramic craftsmanship in multiple ways related to clay processing, workability and firing, and thus reflects a new technological style by people maintaining ceramic forms that recall their home. The differences in li are even more stark and continue to reflect further delineations in social boundaries between Panlongcheng residents and Zhengzhou. In her study of food and belonging in contexts of migration, Abbots (2016) remarks upon the ways in which migrants employ food to reconstruct and stabilize their identities in a new setting by contrasting their food practices against those of the “host” communities. In doing so, food can help stem a sense of loss by “maintaining continuities and (re)embedding the migrant in their home community… while also enabling change to occur and facilitating the creation of new relations of being and belonging” (Abbots 2016: 122). Along those lines, it is evident that Panlongcheng residents maintained some consistency with their cooking tools - the characteristic li tripod shape suddenly appears, replacing the guan cooking pot of the preceding Erlitou culture, and continues throughout the settlement of Panlongcheng. Despite the similarities, however, the difference of li between the two sites have also been well-noted - while both are the classic tripod shape, Panlongcheng li are redware with tall, narrow legs and taller bodies, Zhengzhou li are grayware with shorter and more stout bodies (that become even more short and bulbous through the Yinxu period). The results in this chapter provide additional evidence of changes to the li, this time from a technological and functional perspective, and reflect not only changes in the chaîne opératoire of ceramic production but also the chaîne opératoire of cooking as well. With 142 both formal and technological differences underlying li cooking pots, alongside new food resources, it is worthwhile to consider that Panlongcheng residents gradually developed new cooking techniques and cuisines that would have differentiated them from their compatriots to the North despite the continuation of its general form. The li tripod is therefore a clear symbol of the diasporic “dual consciousness”, as its distinctive shape maintains a continuity and connection to the Central Plains while everything else – its color, height, paste, chaîne opératoire, functional qualities and usage – underwent transformation under the influences of this new context and reflect a culturally significant shift related to changes in cooking practices. While the untempered ceramics and li cookware both represent a blending of both old and new, gang represent the development of something uniquely local. Since gang remains are far more prevalent at southern sites, it has been assumed that this was a southern style. The results reinforce that the gang is a southern ware and that it has a unique paste containing large quantities of sand despite not requiring the thermal shock resistance of a cooking vessel. The petrographic information further indicates that the gang samples found at Zhengzhou were made in the south and imported. Since Zhengzhou had a formidable ceramic industry, they most certainly had the means to craft their own gang, even if they had to adapt their raw materials to replicate the size and shape. If northern ceramicists had the means to craft gang, why were gang instead imported from 500 kilometers away? Gang could have been used as vessels for transporting goods, like the large amphorae of Ancient Rome, and were therefore preferred storage vessels by 143 Panlongcheng travellers. They may also have been imported because of their overall association with the south – if their ceramic industry was known for production of this ceramic type, it may have been desirable, perhaps even prestigious, to acquire them from Panlongcheng (or other sites in the south). They could have been desired for its red fabric, a characteristic of southern wares that makes them immediately distinguishable from the north, or their technological qualities due to their unique and sandy paste. With petrographic confirmation of gang movement from Panlongcheng to Zhengzhou, it is apparent that Panlongcheng had its own ceramic industry that did occasionally flow outward. Rather than simply existing to replicate northern styles, the very development of gang independent from northern influence thus points toward a process of continuity and change within Panlongcheng, with a distinctive ware separating it from the homeland. In summary, while a northern connection is evident through multiple lines of material evidence, there is also evidence of development and change of differing degrees. Relocating to an unfamiliar environment with unfamiliar raw materials would have undoubtedly reworked Panlongcheng settlers’ chaîne opératoire of ceramic production across the board; the raw materials and surrounding resources would have influenced the functional and practical aspects of ceramic (and therefore cooking) technology; and the reworking of and experimentation with the chaîne opératoire may have led to the development of ceramic styles like the gang. This is, of course, not to mention the possibility of influence from local groups who may have introduced new styles to the settlers that were rapidly adopted and adapted. The technological styles present in the assemblage are therefore a representation of a new identity built upon a maintenance of 144 connection with a cultural history blended with the adaptation, experimentation and interaction that comes with a new environment. In the next chapter, we will see further developments in local practices and firing temperature.145 Chapter 6: Exploring Differences in Firing Technology via SEM and Digital Image Analysis Firing is a crucial component in the chaîne opératoire of ceramic production. It is a complex system that can affect stylistic and functional qualities like colour, strength, toughness, hardness and porosity. The results of firing are a function of the firing atmosphere, rate of temperature increase, maximum temperature, length of time at maximum temperature (soaking) and length of cooling, alongside the mineralogical composition of the raw materials. Even in isolation from the rest of the pottery-making chaîne opératoire, the multiple variables involved in firing means the firing process alone can have its own technological style. The complexity of the process and interaction between all of these variables, however, means that the technological style of firing is difficult to fully encompass and parse. Attempts at identifying or reconstructing individual components of the process, particularly firing temperature, have continued for nearly a century using various techniques - physical fabric analysis (Matson 1939; Shepard 1936), experimental archaeology (Ther 2014), ethnoarchaeology (Gosselain 1992), electromagnetism (Bouchez et al. 1979; Rasmussen 2012), optical and scanning electron microscopy (Maniatis and Tite 1981) and more. Scholars have questioned the relevance of isolating firing temperature on its own, as similar firing techniques can result in a wide range in maximum temperatures, sometimes even within the same firing (Gosselain 1992; Livingstone-Smith 2001). Rice 146 (1987) states that maximum temperature is “relatively insignificant unless one knows the duration of the maximum temperature and the atmosphere at that temperature” (81). However, there can be times when disparities in maximum temperature are so stark, they indicate fundamentally different firing techniques - as an extreme example, the difference between terracotta (fired at less than 600 degrees Celsius) and porcelain (fired at 1400 degrees Celsius or above). As such, the ability to broadly differentiate firing temperature can still be useful when viewed as a component of reconstructing chaîne opératoire, rather than the only component needed to deconstruct firing technology. Increases in firing temperature result in mineralogical and textural changes that can be observed in scanning electron microscope backscattered-electron (SEM-BSE) images. This chapter explores such qualitative and quantitative characteristics via SEM-BSE images of Panlongcheng and Zhengzhou ceramics. The previous chapter indicated textural and morphological differences in raw materials between the two sites, suggesting that artisans were faced with new physical properties (and therefore new choices) after relocation that eventually led to the development of a new technological style. While Panlongcheng residents retained certain “Shang” characteristics in their material culture, there were also clear changes in style and production practices represented by, and necessitated by, the new environment. Based on preliminary observations, ceramic production also differed in terms of firing, indicating yet another divergence in ceramic production that occurred post-migration. This chapter begins with a summary of changes that occur in clay during firing and how these can be observed in SEM images, followed with a brief overview of research into 147 firing and the discussions surrounding approaches in studying it. The chapter continues with an analysis of SEM-BSE images of ceramic samples fired in a controlled setting, which are compared to results from samples from both Zhengzhou and Panlongcheng. Quantitative differences of texture and void size will again be conducted via digital image analysis, while qualitative observations regarding changes in structure will also be used to supplement interpretation. While the entirety of the firing process cannot be broken down by image analysis alone, the results from this chapter suggest that firing technology did in fact differ between the two sites and that further experimental research is necessary to reconstruct the full chaîne opératoire. 6.1 Changes During Firing Firing is a crucial component of pottery manufacture -- it is the step that transforms clays into ceramic’s permanently hardened form. The development of porcelain and stoneware, and control around fabric colour and glazes, is largely due to the development of knowledge around firing and firing technology. In terms of microstructure, characteristic changes occur in the firing environment, particularly as temperature increases. The following section summarizes the different changes that occur in clays, while a more detailed description of changes to specific mineral forms can be found in Rice (1987) and Quinn (2022). The firing process can be viewed as an interaction between the firing atmosphere, rate of firing, “soaking” time (time maintained at maximum temperature), maximum temperature and cooling rate. However, while both time of firing and soaking time impact 148 physical and chemical reactions, they are currently difficult, if not impossible, to identify in archaeological ceramics (Quinn 2022: 266). This complicates differentiation of firing technologies that diverge but yield similar physical (and in this case, petrographic) results. Quinn (2022) instead suggests conceptualizing observations in terms of “equivalent firing temperature” that ,“if maintained for one hour produces the necessary energy for a specific reaction or physical change to take place” (266). The “equivalent firing temperature” is therefore a numerical representation of a number of different processes that can all achieve the same end state. The atmosphere, either “oxidizing” or “reducing”, is affected and controlled by the type of fuel used and the way that air circulates within the firing structure. An oxidizing atmosphere occurs when there is more oxygen than needed for the combustion of fuel and it allows for circulation of oxygen within the kiln. A reducing atmosphere, by contrast, limits the amount of oxygen available to bind to the clays, leading to the presence of other gasses such as carbon monoxide. The atmosphere affects a number of different factors such as colour, hardness, porosity and shrinkage (Rice 1987: 81). In some cases, the atmosphere can shift from oxidizing to reducing (or vice versa) during different parts of the firing process. In open-air fire structures like bonfires, the atmosphere is more difficult to control due to exposure to various environmental factors (Rice 1987: 81). The drying of clay is another important step that essentially kickstarts the process finished during firing. Water is initially added to clays during the pottery manufacturing process to increase plasticity and allow forming. Once formed, pottery is typically left to air dry in order to gradually remove “film water” surrounding clay particles, as the rapid 149 removal of such water can lead to a rapid breakdown of the formed body (Rice 1987:64; Quinn 2022: 26). Materials with coarser particles are able to dry faster due to the larger, longer pores that form around the grains as the clay shrinks away from the grains (Quinn 2022: 260). Further heating removes the remainder of the water and causes structural changes in the clay that permanently hardens the material. At the lowest temperatures, water and organic material continue to move from the interior to the exterior of the clay body (Rice 1987: 102). The most major changes occur after losing the water that had been added during forming (hydration), which is when we witness the transformation of the formed clay into a ceramic body. From a petrographic perspective, the clay matrix is still visible and birefringent at this point, appearing as “shaggy” at high magnifications (Figure 42) (Quinn 2022: 409). Smaller particles have yet to be incorporated into the matrix and are thus distinguishable with clear edges at high magnifications as well. Figure 42. Appearance of “shaggy” clay matrix in SEM-BSE. 150 Once the temperatures get high enough, we move into the stage of sintering or “initial vitrification” and, at even higher temperatures, full vitrification. This can depend on the mineralogical composition - iron and mica, for example, may act as a flux and lower initial vitrification to temperatures above 800 degrees (Rice 1987: 94). This is the point at which clay particles begin to join together with other particles, leading to further shrinkage of the ceramic body. Rice (1987) differentiates between solid-state sintering, where the “surface diffusion of atoms between particles forms a join or ‘neck’ between them”, and liquid-phase sintering, where “the constituents of the body begin to melt, forming a liquid” (93). Solid-state sintering is characterized by the presence of spherical or absent pores, where liquid-phase sintering results in pores becoming smaller and particles draw closer together. For both, the smaller particles start to lose their edges as their bodies become more incorporated with the clay matrix (Quinn 2022: 409). Figure 43. Appearance of vitrification in SEM-BSE. 151 Full vitrification is the last stage and typically requires temperatures above 1050 degrees Celsius. The term vitrification refers to a transformation into glass; at this stage, the crystalline structure of clay is transformed as particles melt entirely and form a glassy solid that is impermeable to water (Figure 43) (Quinn 2022: 409). Porcelain and stoneware are the most notable examples of vitrified ceramics. 6.2 Firing Technology in Archaeological Research Archaeologists have long recognized the importance of firing in the study of pottery and pottery technology. Shepard’s (1936) pioneering work meticulously analyzed an array of technological characteristics including clay composition, temper, pigments and also included an estimate of firing temperature. Matson similarly included estimations of firing temperature in his technological analysis (cf. 1939), primarily based on colour comparisons compared to local clays. Research into firing temperature also utilized advanced scientific techniques relatively early. Kondo et al. (1935) reheated sherds soaked in diluted hydrochloric acid and measured the amount of alumina extracted to estimate firing temperature, while Eto (1963) used thermal expansion for his estimation, and Bouchez et al. (1979) explored using magnetism and Mossbauer spectroscopy for their analyses. Evidently the question surrounding firing temperature inspired an array of techniques, many of which are still currently used with progressively advancing technology. The narrow focus on firing temperature however was called into question by Gosselain’s (1992) influential paper “Bonfire of the Enquiries. Pottery firing temperatures in 152 archaeology: What for?”. Gosselain argued that the full significance of firing temperature was rarely clearly defined, focusing instead on the identification of specific temperatures as the goal in and of itself. He also argued that firing procedures and temperatures in any craft production that require it (pottery, metallurgy, glass making) are extremely variable, so identifying only a single temperature is insufficient to make claims about that culture’s firing technologies. Using ethnoarchaeological data, Gosselain illustrates this point - by employing 5-11 probes per kiln at six firings among the Bafia in Cameroon. Gosselain measured the rate of heating and maximum temperature reached. These measurements showed variability across firing and within firings, leading Gosselain (1992:256) to conclude that variability in firing temperature “cannot be explained by specific regional features, such as the arrangement of the structure of the nature of the fuel”. He does, however, argue that based on his results, heating rate and soaking time are instead more suitable parameters for comparison. Nearly a decade later, Livingstone-Smith (2001) produced a follow-up paper titled “Bonfire II: The Return of Pottery Firing Temperatures”, with the aim to improve and expand upon Gosselain’s work, which had a limited data set and some methodological issues. Livingstone-Smith (2001: 991), for example, noted that while Gosselain advocated for heating rate and soaking time as better proxies for regional firing technologies, “the relationships with technical behaviours were not clearly assessed nor explicated”. Livingstone-Smith focused heavily on assumptions regarding “open firing” and “kiln firing”, in particular the expectation that kiln firings (firings involving a permanent structure with a firebox) would naturally reach higher temperatures versus open firings (all other types of 153 structures such as bonfires and hearths). The study used a much more robust sample size, collecting data from 105 firing sessions across several regions and cultures (62 directly monitored and the rest published by other researchers). Livingstone-Smith (2001:999) concluded there was much more complexity to the open/kiln dichotomy and that they are “by no means the major parameter of firing technologies”. For example, his findings indicated that average maximum temperatures fell between 550 degrees C and 950 degrees C for open firings and 650-900 degrees C for kilns, pointing to a great deal of overlap Livingstone-Smith (2001:998). The average duration of firing also overlapped substantially. Ultimately, Livingstone-Smith’s research appears to have echoed Gosselain’s work, concluding that “it is impossible to relate a particular set of thermal characteristics (duration, maximum temperature, heating rate and soaking time) to a specific firing procedure” Livingstone-Smith (2001:999). One of the major weaknesses of Gosselain’s work,however, is that his scope of research was confined to firing practices that topped out at around 1000 degrees C. A thorough analysis of firing structures was also conducted by Thér (2014), who experimentally compared 7 different types of structures (21 different subtypes) totalling 72 firings. Once again expanding on the open/kiln dichotomy, Thér compared bonfires, pit firings, and various constructions of single- and two-chamber kilns. Thér’s research continued to validate Gosselain and Livingstone-Smith’s conclusions that there is a great deal of variability in temperature across various forms of firing structures, and firing temperature alone is not often a reliable proxy to recreate firing process. That said, Thér did identify some key differences between firing structures: 154 1) While bonfires are able to reach maximum temperatures similar to those of insulated kilns, the ability to control the temperature is much more challenging; 2) As such, heating rate is a main differentiator between bonfires and structures with insulation; 3) One- and two-space firings are also differentiated by heating rate; 4) Maximum difference in temperatures (i.e., variability) also differentiate the different structures into discrete groups. Based on these findings, one might conclude that it is rather meaningless to estimate firing temperature from sherds. However, a main conclusion from these studies is that there can be a great deal of variability in firing temperature but, based on Thér’s analysis, some structures are less variable and more controllable than others. As such, we may ask: if we find ceramics at a site that are consistently around a similar equivalent firing temperature, what does that say about that site’s firing technology and technological processes? Researchers have indeed continued to pursue the identification of firing techniques and temperatures through increasingly advanced scientific methods such as the use of colour coordinates (Mirti and Davit 2004), measurement of magnetic susceptibility (Rasmussen 2012) and Raman spectroscopy and Fourier Transform Infrared (FT-IR) (Freitas et al. 2017). One of the most common ways of examining firing temperature is through the examination of ceramic thin-section via SEM (often in conjunction with X-Ray Diffraction and/or X-Ray Fluorescence). While clay composition varies widely across sites, 155 the microstructural changes observed over the course of increasingly higher temperatures is fairly consistent (with some notable exceptions such as kaolinite). Maniatis and Tite (1981) used SEM to examine the microstructural changes of sherds across central and southeast Europe from the Neolithic to the Bronze Age, both in their original forms and after refiring at higher temperatures. The “as-received” forms provided information on both pore structure and microstructure at original firing temperatures, where the changes after refiring provided a basis for understanding subsequent changes as the sherds approached and surpassed vitrification. This allowed the authors to estimate firing temperature and atmosphere, suggesting ways in which one may identify different firing procedures and technological approaches. One of the crucial conclusions drawn by Maniatis and Tite (1981) was the difference that occurs between calcareous (containing >6% calcium oxide) and non-calcareous clays. While the “initial vitrification” stage, where glass begins to form, occurs at around the same temperature for both types of clay (~800-850 degrees C), the subsequent glass structure (“extensive vitrification”) associated with higher temperatures depended greatly on the type of clay. For non-calcareous clays, the glassy filaments that occur at initial vitrification grow steadily with higher temperatures, eventually melding together into a fully vitrified surface within 150 degrees C. For calcareous clays, glassy filaments increase slightly but remain essentially unchanged for 200 degrees (850-1050), whereupon the glass content increases rapidly to complete vitrification. The size and distribution of calcium oxide is also influential on the development of the vitrification structure, as fine-grained and more uniformly distributed concentrations will establish a stable structure whereas 156 larger calcium oxide concentrations could have differing structures within the same pottery sample. Notable also was Maniatis and Tite’s (1981) findings on the influence of firing atmosphere. For non-calcareous clays, early vitrification stages occur at ~50 degrees lower due to the stable crystalline phases that form when oxygen is lacking. Small (<10 μm), spherical pores (“fine bloating pores”) also develop and are particularly pronounced with rapid heating temperatures (Figure 44). These pores gradually grow larger, in reducing numbers, until there are a handful of larger (10-50 μm) unconnected pores by around 1050-1100 degrees C. In an oxidizing atmosphere, the small spherical pores do not develop unless the pottery is initially fired in a reducing atmosphere and is later changed to oxidizing. Coarse bloating pores can occur in both atmospheric conditions when gas that forms in the melting body cannot escape (Figure 45). Figure 44. Fine bloating pores. 157 Figure 45. Coarse bloating pores. For calcareous clays, the atmosphere has a less drastic influence. The early vitrification stages also occur at ~50 degrees C lower, but otherwise temperatures for the remaining changes are similar to those outlined above. When the CaO distribution is non-uniform and in a high concentration (>15%), bloating pores will occur but will not grow from 900-1050 degrees. The coarse bloating pores will occur at high temperatures as per above. Maniatis and Tite’s analysis has been validated and expanded upon by other researchers, using SEM, XRF and XRD to elucidate differences in mineralogical changes in non-calcareous and calcareous clays (cf. Cultrone 2001; Trinidade 2009), and similar refiring experiments for other regions have also been employed to estimate firing temperature (Bland et al. 2017; Velraj et al. 2009). Growing knowledge of geochemical 158 transformations (e.g., 900 degrees C as the melting point of muscovite, partial melting of quartz at 950 degrees C) has also been employed to more precisely estimate firing temperature via SEM-related analyses (cf. Ionescu and Hoeck 2011; Ionescu et al. 2011). This study uses digital image analysis of SEM-BSE images to broadly differentiate between “high-fired” (reaching initial vitrification) and “low-fired” (not reaching initial vitrification ceramics) ceramics at Panlongcheng and Zhengzhou. While specific minerals are not identified, general phases for each sample can be isolated and their relative abundances can be compared. The problem of identifying specific firing temperatures is therefore circumvented, as this over/under differentiation most certainly points to different firing processes. Void morphology is also used to explore changes (or lack thereof) in void size, as well as the identification of micropores that point toward specific firing conditions. 6.3 Methodology This analysis first examines sample sherds fired at standard temperatures to explore how broad temperature changes can be tracked via digital image analysis. As part of an unrelated study, Anyang loess paleosol was processed and used for experimental pottery. Part of this material was reserved and fired in a laboratory kiln from 300 degrees C to 1100 degrees C at 100-degree intervals in oxidizing conditions, with a soaking time of 5 hours for each sample, and thin-sections for each of these samples were taken. Based on qualitative microstructural analysis, this material closely resembles “untempered” samples found at both Zhengzhou and Yinxu. 159 Two images from each temperature were analyzed, one at 1000x magnification and one at either 8000x or 5000x magnification (depending on the availability), for a total of 20 images. The 1000x magnification allows a relatively broad field of view to examine structural changes, while the 5000x/8000x magnification allows for close-up examination of changes to the clay matrix. Similar pre-processing measures as those in the preceding chapter were taken - for each image, the scale was set and removed and slight adjustments to contrast and brightness were made if necessary. The analysis examined 36 images (18 samples, at 1000x and 8000x/5000x magnification; see Appendix B for images analyzed). Once again, images were only used if taken in a central part of the sample (i.e., sample edges were excluded). In this case, 6 of the samples were li, 5 were gang, 6 were “untempered” (pen, guan, gui, da kou zun, zeng) and a “protoporcelain” sherd from Panlongcheng was also included (Table 6). Type Site # of Samples # of Images Gang Panlongcheng 3 6 Gang Zhengzhou 2 4 Li Panlongcheng 3 6 Li Zhengzhou 3 6 Protoporcelain Panlongcheng 1 2 Untempered (guan, zeng, da kou zun) Panlongcheng 3 6 Untempered (guan, gui, pen) Zhengzhou 3 6 18 36 Table 6. Number of Images per Site and Type (Firing Analysis). 160 6.4 Training and Segmentation As with the previous section, each image was segmented using the Trainable WEKA Segmentation (TWS) package and used the same pre-trained classifier as well. Five classes were again identified, but the grain class (#2) aligning primarily with silicates was reclassified to “glass” due to similarities in grayscale value. Due to the extremely low quantities of this class in the original classification, this adjustment had little impact on the overall quantification of each group. As such, where all of the grain classes were originally separated and recombined into a single image in the original analysis, only quartz + potassium feldspar were recombined and silicates/glass was left as a separate image. Approximately 10 sections of ~40-50 pixels for each class were selected and the classifier was retrained, with iterative training until the segmentation was found suitable. The “Get Probability” option was then selected, producing a stack of images. The separate images for grain classes quartz + K-feldspar were added together (Process -> Image Calculator). This resulted in separate images for voids, minerals, matrix and silicates/glass ready for thresholding (Image -> Adjust -> Threshold -> Otsu). The area covered by objects in each image was taken (Analyze -> Measure) and the percentage area of each image out of the total area of the image was calculated (Area Image/Total Area*100). Additionally, void morphologies collected in the previous section were used to identify the presence small closed pores in each sample, which have a direct relationship to firing temperature and atmosphere (Quinn 2022: 409). 161 6.4.1 Results 6.4.1.1 Area Percentages The results reflect the general process of microstructural change described by Rice (1987) and Maniatis and Tite (1981) (Figure 46). The percentage of minerals fluctuates throughout the process between approximately 20-40%. The plot shows a general decreasing trend as the minerals become more coalesced with the rest of the ceramic body, but since quartz and potassium feldspar are still identifiable near total vitrification, they still register a sizable amount of the total area (Figure 47). Voids stay under 20% of the total area but gradually increase starting at around 575 degrees C as other components (minerals and clay matrix) begin to shrink, until we see a rapid drop-off at 1100 degrees C when the more interconnected voids quickly disappear (due to the expansion of glass) and isolated coarse bloating pores appear instead. Figure 46. Changes in area percentages - temperature. Glass increases rapidly after ~850 degrees C. 162 Figure 47. Vitrification – some particles still visible but integrating with the rest of the body. As expected, the most drastic changes occur with the clay matrix and amount of glass. As the temperature increases, the clay matrix steadily decreases from around 40% to around 18%. Meanwhile, the amount of glass starts at approximately 13% of the total (represented primarily by silicates rather than glass) and increases to nearly 60% by 1100 degrees C as it approaches total vitrification. Considering the gradual increase of glass over the course of the firing process, as opposed to a stable vitrification structure spanning ~850-1050 degrees C, this suggests that the clay is non-calcareous. These percentages are specific to these samples, the firing atmosphere and this clay composition; as such, it isn’t possible to generalize using the raw numbers to infer firing temperature for samples from other areas. However, this information can be used to identify broad trends - specifically, the difference between vitrified samples and non-vitrified samples. A fairly simple heuristic is to identify the point at which the clay matrix 163 and plagioclase/glass intersect, as this is around the temperature at which initial vitrification occurs (Figure 48). The temperature identified on Figure 48 (~880 degrees C) aligns with what is specified in the literature for vitrification of non-calcareous clays in an oxidizing atmosphere (850-900 degrees), but, again, this temperature may vary depending on the factors above. Rather than focusing too heavily on the specific firing temperature, the heuristic can be used to categorize samples into two categories: samples that have higher percentages of glass compared to clay matrix can be considered “high-fired” as they have undergone vitrification, whereas samples with higher percentages of clay matrix compared to glass can be considered “low-fired” as the clay matrix has yet to undergo this transformation. Figure 48. Changes in area percentages - matrix/glass. When samples have a higher percentage of glass in the image, one can consider it “high-fired”; conversely, “low-fired” samples have a higher percentage of matrix. 164 When this heuristic is applied to the results from the Panlongcheng and Zhengzhou samples, we can identify a clear difference in firing (Figure 49). Zhengzhou samples are typically high-fired, with both li samples and untempered samples indicating high-firing in every case. Both gang samples meanwhile indicate low-firing. In the previous section, the grain-size distributions of gang samples highly suggest a Panlongcheng origin. The firing results further solidify this conclusion. The li, untempered samples and gang from Panlongcheng are all low-fired, with large amounts of clay matrix dominating at around 40% of the total area in most cases. Two exceptions are the zun and protoporcelain sample. Both zun (stoneware) and protoporcelain (theorized to be a precursor to porcelain that we see arise in southern China in later periods) are high-fired “hard wares” typically associated with southern production and thus align with these results. Figure 49. Percentage of matrix versus glass by site and type. Zhengzhou samples are all high-fired; Panlongcheng samples are almost all low-fired except for stoneware zun and protoporcelain samples. 165 6.4.2 Voids 6.4.2.1 Cumulative Frequency Graph When plotting the cumulative area frequency of geodesic diameter by temperature (Figure 50), there appears to be little direct relationship between the distribution of void length/size and temperature. However, Rice notes that it is difficult to identify firing temperature from void length and volume for lower firing temperatures, but is clearer once initial vitrification begins (Rice 1987:429). Void volume typically maxes out at 800 degrees and can be maintained up until 1000 degrees, whereupon open pores start to close and overall pore volume shrinks rather rapidly as clays and particles fuse together. When grouping temperatures into discrete categories, one can more clearly see that the maximum lengths of pores occur 800 and 900 and shrink back down at 1000 and 1100 (Figure 51). As noted earlier, void length and area are reflective of the size of inclusions due to the ways in which clay shrinks away from large clasts during firing (see again Figure 41). This is consistent with what Rice (1987) outlines regarding interconnected pores (350-351). As such, it is not possible to identify exact firing temperature from pore volume alone via digital image analysis since other factors will influence the existence of pores and pore length. However, one can infer the stage of vitrification based on the amount of interconnected pores remaining. Based on the results of the previous section, Zhengzhou’s void volume for all types was relatively high (~18-26%) with the presence of long, interconnected pores, indicating it had reached vitrification but remained under 1000 degrees. 166 Figure 50. Cumulative frequency of void size by temperature. Figure 51. Cumulative frequency of voids highlighting temperatures above 800°C. Voids grow to their longest at 800°C-900°C but rapidly shrink as clay starts to vitrify. 167 6.4.2.2 Morphology - Micropores Closed pores can also be a reflection of firing temperature but, as detailed in Maniatis and Tite’s (1981) research, firing atmosphere plays the biggest role - with calcareousness as a secondary influence - when it comes to the presence of closed spherical pores <10 μm in length (“micropores”). Nan et al.’s (2008) comparison of the composition of bronze casting clay cores from Panlongcheng and northern sites (Yinxu, Houma, Lijia and Zhengzhou) indicated that CaO and Mg concentrations were potential signatures to differentiate between the two regions. Their study found that northern clay cores had higher compositions (>1%) of CaO and Mg, while Panlongcheng clay cores were lower in calcium and magnesium content (<1%) (Nan et al. 2008: 78). They also compared these clay cores to local soils and found that CaO concentrations in northern loess ranged from 3.52%-19.92% (Nan et al. 2008: 78). As such, it may be the case that Zhengzhou ceramics had a suitably high enough concentration of CaO to result in some micropores at high temperatures. However, Liu and Chen (2012: 288) noted that, based on personal communication with other scholars, these results are controversial. For the purposes of this study, we will consider both CaO concentrations and reducing atmospheres as possibilities for the presence of micropores. In order to identify the presence of micropores, voids were limited to those <10 μm in Feret diameter, Area>2.5μm2 (to filter out any noise) and >0.7 Circularity. While micropores are considered “spherical”, realistically they do vary in shape and are more accurately described as approximating a spherical shape. As such, setting the threshold at 0.7 avoids excluding relevant but not perfectly spherical pores. Since the sample images 168 being compared are the same size and magnification, I compare direct counts of micropores to assess the differences in micropore concentration between samples. When analyzing the temperature samples, the number of micropores (at 1000x magnification, allowing for a broader field of view) does not vary much across temperature (13.5 +/- 4 micropores) (Figure 52) and the concentration of micropores is low across temperatures as well. This is consistent with Maniatis and Tite’s (1981) findings of samples fired in oxidizing environments. When examining samples from Panlongcheng and Zhengzhou using the same filters, this time at 100x magnification to allow for an even larger field of view, the da kou zun sample (UBC06-42) dominates the count of micropores (Figure 53). Along with its extremely high firing temperature, this suggests that the sample was fired (even temporarily) in a reduction environment with little CaO. Figure 52. Number of micropores per temperature sample (500x magnification). 169 Figure 53. Number of micropores per site sample (100x magnification). Panlongcheng’s stoneware zun sample has by far the highest amount of micropores. Micropores develop as a function of temperature, atmosphere and composition (presence/absence of CaO). When excluding UBC06-42, Zhengzhou samples dominate in terms of the presence of micropores. 6 out 10 Zhengzhou samples have >200 micropores, whereas only 2 out of 14 Panlongcheng samples are at the same concentration (Figure 54). Once again, the presence of micropores typically indicates firing at a high temperature in a reducing environment, unless the clay contains a large concentration of CaO (>15%). 170 Figure 54. Number of micropores per site sample - excluding UBC06-42 (100x magnification). Outside of Panlongcheng’s stoneware zun sample, Zhengzhou samples tend to have higher quantities of micropores. Zhengzhou wares are typically referred to as “grayware” due to their dark fabric (often in gray to dark gray range on the Munsell chart), which is also an indication of firing in a reducing environment. The presence of micropores in Zhengzhou samples is, therefore, consistent with non-calcareous clay being fired in a reducing environment, a high concentration of calcareous clay being fired in a reducing environment, or the samples being initially fired in reducing environment. Meanwhile, Panlongcheng ceramics are usually redwares, indicating iron-rich clays fired in oxidizing environments (reddish-yellow/reddish-brown/brown). It appears as though most samples were not fired at high enough temperatures to establish micropores to begin with, but the firing atmosphere may differ from that typically used in Zhengzhou ceramic production. 171 As noted, non-calcareous clays can start to vitrify at ~50 degrees lower than what is typical, i.e., around 750-800 degrees, when in a reducing environment. Firing in a reducing environment can, therefore, be more efficient when the properties of high-fired ceramics are desired, as it requires less fuel to achieve vitrification. Additionally, micropores become more pronounced with a rapid increase in firing temperature. For the samples with particularly high counts of micropores, it’s possible that these samples were subjected to rapid heating increases. Considering the number of micropores in the da kou zun sample (UBC06-42) found at Panlongcheng, the pottery was likely fired in a reducing environment with little CaO and was subject to rapid increases in heating. This may indicate that da kou zun were fired in different environments than other ceramics at Panlongcheng and that there is a difference in CaO concentration between Zhengzhou and Panlongcheng. A suitably high amount of CaO would explain why the micropores at Zhengzhou don’t come close the levels of the da kou zun at Panlongcheng, even though Zhengzhou wares were likely high-fired in a reducing atmosphere (since CaO would arrest the vitrification of the crystalline structure from ~850-1050 degrees C and therefore the further development of micropores). If this is the case, Panlongcheng residents specifically chose to fire their utilitarian ceramics in oxidizing atmospheres – another indication of shifts in technological style. Another possibility is that the Zhengzhou wares were not subject to as rapid heating, as they were not as high-fired as the da kou zun. Further research on local CaO concentrations in Erligang ceramics is required for clarity, and future excavations concentrating on kilns and production facilities at Erligang sites would be incredibly beneficial as well. 172 6.4.2.3 Random Forest Classifier - Void Morphology I employed a random forest classifier to determine whether Panlongcheng and Zhengzhou samples can be predicted based on small void morphology (using Geodesic Diameter, Feret Diameter, Circularity, Convexity, Tortuosity and Average Thickness as the predictor variables). Convexity is similar to Solidity, as it is the ratio of the Convex Hull/Total Area. An irregular shape will have a lower convexity value and something perfectly solid will have a value of 1. Tortuosity is the measurement of sinuosity - that is, a measurement of the overall flow path of the pore. While Geodesic Diameter is the shortest path from one end of the pore to the other, Tortuosity measures the total path (Length of the pore/Length of the straight measurement of the pore) (Figure 55). Figure 55. Tortuosity calculation to measure the total path of an irregular line. Using the random forest classifier can also indicate if there is a significant difference between the two sites in terms of small void morphology. A model was trained on li and untempered samples. Data were limited to pores 0.5 - 10 μm in Feret Diameter inclusive and Area>2.5μm2 (to limit stray pixels). Given there are two gang unearthed from Zhengzhou but are speculated to be from Panlongcheng, all gang samples were excluded 173 from training. Instead, after training and validation, the model is used to predict the manufacturing sites of each gang sample. All of the following was done using the ranger and caret packages in R. Random forest is a non-linear model that aggregates the results of multiple (often hundreds of) decision trees that are each built using a subsample of the available data. It first divides the predictors into regions, split to minimize the GINI index (a measure of variance) for classification, and then calculates the mode outcome values for that selection of data. Random forest automatically uses an “out-of-bag” (OOB) error that is common with machine learning models that use bootstrap aggregation (also known as “bagging”), which creates separate subsamples from the same dataset for both training and testing simultaneously and repeats this process many times. The OOB provides an error rate of incorrect classification of the out-of-bag samples and is a good indicator of the strength of the model. The accuracy of the model however can be improved even more by setting aside “test” data to be used as validation after the training of the model. In this case, I randomly set aside 30% of the data for testing purposes (n=11704, Panlongcheng n=6238, Zhengzhou n=5466). Using the remaining 70% of the data (n=27310, Panlongcheng n=14461, Zhengzhou n=12849), I tried different models from 200 to 500 trees in intervals of 50. Both the node size and the number of variables tried at each split (mtry) were set to default, as the number of available variables was relatively limited. The best OOB error came from 450 trees at 38.65%, i.e., around 61% of the samples were predicted correctly. Running the test data, the accuracy of the model was 62.12%. In 174 comparison to the 53% No Information Rate (NIR), which uses the most frequent class (Panlongcheng) to predict all observations, this model is a significant improvement (p<0.01). The model’s sensitivity (true positive rate) was estimated at 69.16% and its specificity (true negative rate) was estimated at 54.10%, with a balanced accuracy of 61.63% (Table 7). Evidently the model has a difficult time correctly predicting Zhengzhou voids, often predicting them as Panlongcheng. PLC ZZ PLC 4299 2508 ZZ 1939 2958 Table 7. Confusion Matrix of Random Forest Test Predictions. While the model appears to lack predictive strength, these results are understandable. Although Zhengzhou samples tend to contain high concentrations of spherical micropores, they also contain other pores that could be mistaken for those in Panlongcheng samples. Conversely, the high true positive rate indicates that Panlongcheng samples tend to have fewer pores that would be mistaken for Zhengzhou pores. Extracting the variable importance from the model, it appears as though Average Thickness is, somewhat surprisingly, the variable that has the biggest impact on model accuracy and Circularity is the second most important, though all of the variables except tortuosity seem to play a role (Figure 56). Rather than expecting the model to be able to predict sites based on individual pores, I use the model to predict each sample in aggregate by looking at the total number of predictions per sample. 175 Figure 56. Variable importance for random forest model. For example, running the model on UBC06-01 (a li sample that was almost certainly manufactured in Zhengzhou based on its fabric and grain-size distribution), the model predicts 83.3% (2171/2607) of its pores as Zhengzhou and 16.7% (436/2607) as Panlongcheng. Similarly, the model predicts UBC06-34 - a Panlongcheng li sample - to be of Panlongcheng production (1187/1644 or 72% of pores classified as Panlongcheng). The only incorrect prediction appears to be UBC06-42, the high-fired Zun with high numbers of micropores seen in Figure 53. As such, it appears as though spherical micropores are highly associated with Zhengzhou wares. See Table 8 for the remainder of the predictions. As stated, the gang samples were purposefully left out of training. Since the random forest classifier is a supervised learning model, meaning the “ground truth” (in this case, sites) of the data is already known and identified, I would have had to pre-classify some gang samples that were unearthed in Zhengzhou but speculated to be of Panlongcheng origin. As such, all gang samples are used to see whether they would uniformly be 176 predicted to be from Panlongcheng based on li and untempered training data. Based on the table above, all of the gang samples were also predicted to be from Panlongcheng based on pore morphology regardless of excavation site. This indicates that small void size and shape are different enough at either site, largely due to the differences in firing practices, to differentiate using Random Forests classification. Sample Site Type Predicted PLC Predicted ZZ Prediction UBC06-01 ZZ Li 436 2171 ZZ UBC06-02 ZZ Li 396 1286 ZZ UBC06-03 ZZ Li 446 2411 ZZ UBC06-04 ZZ Untempered (Guan) 586 1956 ZZ UBC06-05 ZZ Untempered (Guan) 646 3668 ZZ UBC06-06 ZZ Gang 2247 1274 PLC UBC06-08 ZZ Gang 4406 2024 PLC UBC06-10 ZZ Untempered (Gui) 542 4352 ZZ UBC06-11 ZZ Untempered (Pen) 1075 3668 ZZ UBC06-12 ZZ Gang 2204 1169 PLC UBC06-17 PLC Li 841 395 PLC UBC06-20 PLC Zun 2885 1023 PLC UBC06-21 PLC Gang 975 610 PLC UBC06-22 PLC Gang 1281 930 PLC UBC06-24 PLC Gang 2217 1669 PLC UBC06-26 PLC Gang 1479 786 PLC UBC06-28 PLC Li 1076 364 PLC UBC06-29 PLC Li 837 305 PLC 177 Sample Site Type Predicted PLC Predicted ZZ Prediction UBC06-33 PLC Gang 3051 1454 PLC UBC06-34 PLC Li 1187 457 PLC UBC06-35 PLC Untempered (Pen or Gui) 58 33 PLC UBC06-38 PLC Untempered (Guan) 2305 739 PLC UBC06-39 PLC Untempered (Pen) 1851 362 PLC UBC06-41 PLC Untempered (Zeng) 2827 697 PLC UBC06-42 PLC Untempered (Da Kou Zun) 5059 5315 ZZ UBC06-43 PLC Li 2594 559 PLC UBC06-51 PLC Untempered (Likely Pen) 5013 1081 PLC Table 8. Random forest model - void morphology prediction results. The model correctly predicts each site based on morphological characteristics of the voids for all except UBC06-42, the stoneware zun with the highest quantity of micropores. 6.5 Discussion Although firing practices can be difficult to reconstruct, these results have offered possibilities for discerning details of the chaîne opératoire of firing at both Panlongcheng and Zhengzhou. Changes in proportions of basic mineralogical composition helped to establish a simple heuristic for determining and quantifying high- and low-fired ceramics via digital image analysis, and morphological analysis of closed pores provided further information about firing temperature, firing atmosphere, and possibly firing rate. These results further solidify differences in technological choices between Panlongcheng and Zhengzhou ceramicists, differences that likely have further effects related to ceramic function and performance. 178 The most obvious difference is that Zhengzhou ceramics are overwhelmingly high-fired regardless of type. The only exceptions are gang unearthed in Zhengzhou that, as noted, likely originated in Panlongcheng based on their grain-size distribution, grain morphologies and void characteristics. These differences in temperature further indicate that the gang were likely imported to Zhengzhou rather than being locally-made. By contrast, Panlongcheng ceramics are overwhelmingly low-fired and do not come close to vitrification or even early vitrification. The clay matrix of Panlongcheng ceramics comprise a large proportion of its overall composition and retain a “shaggy” appearance in SEM-BSE images. Two notable exceptions are a stoneware da kou zun sample and a protoporcelain sample, both of which are high-fired ceramics associated with southern production. These samples indicate that the Panlongcheng residents had the capacity for high-firing and, in the case of the da kou zun, possibly in a reduction environment. As noted earlier, since firing temperature can vary greatly in many different firing structures, consistent results are notable because it represents a well-regulated process. It is evident that Panlongcheng residents rarely fired their ceramics above 800 degrees unless they were producing very specific types of pottery. Zhengzhou residents, on the other hand, never fired their ceramics under 800 degrees based on these samples. While firing temperature alone cannot encompass the entirety of the firing process, one can infer based on the consistency of these results that the processes between the two sites were indeed different and deliberately so. The divergence in equivalent firing temperature would have most likely led to functional differences. Tite et al. (2001) note that pottery generally has two to three basic 179 functions: 1) retaining contents (determined by fracture strength), 2) surviving impact (determined by toughness) and sometimes 3) withstanding rapid changes in temperature (determined by thermal shock resistance). Firing temperature influences these properties greatly due to the increased bonding between clay particles caused at progressively higher temperatures. As the bonds strengthen, the overall strength of a clay pot increases - but the ability for a high-fired ceramic to survive crack propagation (toughness) is lower since the energy needed to initiate a crack is much higher. Conversely, low-fired ceramics are not as strong but are better equipped to survive crack propagation. The ability to withstand crack propagation is an important component of thermal shock resistance, as exposure to rapid heating can cause cracks due to temperature differences between the inner and outer surfaces of the vessel. Thermal shock resistance is also improved by larger inclusions (indigenous or added temper), which arrest crack propagation and dissipate the kinetic energy of the moving crack. Higher porosity, including pores that develop around temper as the particles shrink during heating, will also help thermal shock resistance as they also aid in arresting of cracks. However, strength decreases as the size and the concentration of temper increases (Tite et al. 2001: 308-309). The interactions between firing temperature, temper, mineralogical composition and morphological properties (such as wall thickness) are a balancing act of benefits and compromises. Generally speaking, a high-strength ceramic would be high-fired with low amounts of temper, while tough and thermally resistant ceramics would have high amounts of temper and a low firing temperature. Other properties, such as wall thickness 180 or surface treatments will have further impacts. Due to these additional variables, conclusions we can draw regarding Panlongcheng and Zhengzhou ceramics are only tentative and require further research and experimentation. We can, however, still identify some potential changes in performance characteristics based on these results. Given the results presented in the previous two chapters, untempered Zhengzhou ceramics should have high strength due to their high firing temperature, but their li cooking vessels may easily crack because of the large quartz temper combined with high firing temperature. Untempered Panlongcheng ceramics are generally low-fired with low amounts of temper, compromising strength and also toughness. Panlongcheng li ceramics on the other hand have some fine-, medium- and very-coarse sand particles and are low-fired, improving both toughness and thermal shock resistance, while gang are also tempered (though typically with smaller temper particles than li within this field of view) and low-fired as well. Ceramic recipes are not necessarily developed to maximize functional aspects. Raw materials, convenience, tradition and habitus are all factors in technology and manufacture. Deliberate or not, however, the functional characteristics resulting from these recipes can most certainly have influence on other aspects of the chaîne opératoire, such as the use and discard of the vessel. In the case of the untempered vessels, the collected data suggest Panlongcheng’s vessels would be better equipped for transport as they resist impact (higher toughness), but Zhengzhou’s vessels would be able to retain heavier materials (higher strength). It is difficult to determine whether these differences would be significant enough in practical use to impact daily use. As Tite et al. (2001: 318) 181 note, strength and toughness are essential characteristics but the requirements for most needs will be sufficient via most firings and recipes. On the other hand, there is ethnographic evidence that vessel strength is a desirable and known quality, and that certain producers will be favored for their strong ceramics if that is a needed characteristic (e.g., Aronson et al. 1994). As such, the firing temperature difference of Panlongcheng and Zhengzhou untempered ceramics may well have been driven by performance characteristics but they also could have been driven by other factors like fuel availability, formal qualities, external influence, and so on. The performance characteristics of cookware have a more direct impact on regular use. If Zhengzhou li are indeed more prone to breakage, this would require more controlled heating of the vessel during cooking or more active li production if breakage were accepted as a regular occurrence. The low-fired, more evenly tempered li from Panlongcheng, on the other hand, would be more resistant to crack propagation, making the vessels more durable for cooking. It is generally accepted that the tradeoff for low-fired, high-tempered vessels is high porosity, making a vessel more permeable with lower heating effectiveness. Based on cumulative frequency and total area percentage of voids, Zhengzhou li are more porous than those at Panlongcheng because of the large and numerous clasts used to temper the vessels. Thus, when assessing based on petrographic analysis alone, the li at Panlongcheng have improved durability and heating effectiveness, which would potentially influence (or were influenced by) the cooking practices and procedures of the Panlongcheng migrants. 182 These differences also have social and political implications. Reinhardt and Edge’s 2020 study on Yanshi Shangcheng ceramics indicated that samples from elite contexts were fired at even higher temperatures based on hardness tests (the raw materials appear to be identical). If this is the case in Zhengzhou and other Central Plains sites as well, the shift to low-fired wares in Panlongcheng indicates a stark change in the social value of high-firing. The samples in this study are primarily from more elite contexts (Yangjiawan near F4, Yangjiazui M26) but are still entirely low-fired except for protoporcelain and stoneware samples. This may indicate focused attention on protoporcelain and stoneware as markers of prestige, influences from local groups, other material qualities reflecting high status (e.g pottery gradually becoming redder over time) and/or practical considerations like fuel availability. The low-fired nature of utilitarian wares at Panlongcheng would therefore have influence on the chaîne opératoire of pottery-making, the performance characteristics of wares, and the reflection of hierarchy in Panlongcheng, all of which represent the development of new regional practices and identity, individually and in aggregate. 6.6 Conclusion The results of this section continue to highlight the integration of both old and new traits in Panlongcheng ceramic production, and the resultant blended identity reflected in Panlongcheng technological activity. As noted in Chapter 5, the changes of each ceramic type highlight different ways in which new social boundaries are manifested via shifts in technological style (i.e., changes in raw materials, functional differences that impact use, 183 prestige, and the development of uniquely local ceramic types), and as such the ceramic assemblage reflects Panlongcheng holding both its homeland and its new home together as a uniquely local blend. The changes in firing technology add another layer to this, as we again remember that the general formal ceramic traits of Zhengzhou are mostly maintained, while the active process of creation and of use changed substantially at Panlongcheng. The abundance of high-fired wares found further south down the Yangtze and the heavy integration of these wares into Panlongcheng assemblage may also reflect some cultural interaction and the exchange of ideas and techniques. If we are to accept that the how is just as, if not more, important than the what in identity-making, the Panlongcheng ceramic production process reflects the “new social distinction” borne out of a diasporic experience of maintenance and change (Cipolla 2017:3). Rather than viewing Panlongcheng residents as a simple colony diligently replicating the wares due to influence from the capital, we can consider that the residents maintained some connections with the homeland – both directly and through habitus – while their technology diverged in several ways. In some cases, such as soil composition, this is primarily due to the availability of materials in their new environment. However, in the case of firing technology, we know - from the presence of high-fired stoneware and protoporcelain - that Panlongcheng residents specifically chose to low-fire their everyday wares. Such choices impacted not only the chaîne opératoire of production but also the functional and social qualities of their pottery, and actively differentiates their ceramic 184 technological style while still maintaining certain ceramic forms that echo the Central Plains. With li in particular, they maintained the shape of the cooking vessel but fundamentally changed the technology and its performance characteristics. While the focus of this dissertation is primarily on the establishment of social boundaries through ceramic production, a major theme in cultural food studies is also the creation and negotiation of social identity (cf. Avieli 2016; Fischler 1988; Ohnuki-Tierny 1994; Scholliers 2001). Archaeologically, the habitus of cooking can be explored through the ways in which cooking technology was repeatedly made and used (Hastorf 2012; Stein 2012; Twiss 2007, 2012); as local groups develop their own practices and habitus for the preparation of available food resources, distinct regional signatures can arise. Thus, while the exploration of technological style in any artifact holds the potential to identify different social and regional boundaries, the examination of instruments related to cooking adds another layer of differentiation. Considering the intimate connection between cooking and identity, the functional changes in li due to new raw materials and different firing behavior could have had a great impact on the evolution of identity for Panlongcheng residents as new cuisines and cooking practices developed. While quotidian pottery was clearly subject to change, it has generally been accepted that the prestigious ritual bronzes maintained identical formal characteristics as those in the north. It has been assumed that metallurgical resources (e.g., copper) were transported to the North and that ritual bronzes cast in Zhengzhou were subsequently imported back to Panlongcheng as a symbol of the maintenance of power for the royal 185 class. Recent metallurgical analysis, alongside ongoing excavations in Panlongcheng, have added complexity to the prevailing model. In particular, it appears as though Panlongcheng had its own local bronze-casting industry. While a great deal of research continues on the metallurgical technology of the bronzes found at various sites, there are fewer comparative analyses on the bronze-casting molds found at both sites. The following chapters present digital image analysis of molds from Panlongcheng and Zhengzhou, once again probing whether raw material resources, firing temperature and other characteristics can be parsed from these ceramic technologies and what this might say about the development of local industry in Panlongcheng. 186 Chapter 7. Panlongcheng Bronze Casting Ceramic Technology Bronze casting in the Central Plains had already been established by the Erlitou Period, predecessor of the Erligang Period. By that point, bronzes of relative complexity, including ritual bronzes, were already being cast using piece-mold casting, the same technique seen during the Erligang and Yinxu periods (Bagley 1977). Bronzes continued to develop in both sophistication and importance during the Erligang Period, becoming increasingly more plentiful and more complex in size and shape, and were circulated widely across a network of Erligang sites (see Chapter 3). During the Yinxu Period, the quantity and diversity of bronzes increased tremendously. The scale of production during this period was immense, with Yinxu having four separate foundries (Xiaomintun, Miaopu, Xiaotun and Dasikong), all located along the Huan River to facilitate water access for transportation and production requirements (Tang 2016). The abundant material from these foundries has provided incredible amounts of information about Yinxu’s advanced bronze-casting industry, including the organization of the foundries, the evolution of foundries over time, and the overall casting process. Crucible fragments, molds, models, ingots, furnaces, grinding stones, and work areas with vitrified surfaces (likely related to casting procedures) (Li 2018: 58) are amongst the rich amount of material that has allowed researchers to build a picture of large-scale bronze-casting during the Yinxu period, and much of what we know about the bronze industries during Bronze Age China is thanks to the remains unearthed at Yinxu. Ceramic technology is a crucial component of understanding bronze-casting in ancient China. The casting method, the piece-mold method, involves applying clay to a 187 prepared model, sectioning off the clay and reassembling the sections around a core. Karlbeck’s (1935) careful analysis of Anyang mold fragments led to a realization that the casting process was piece-mold after he had noticed scorching on the interior surface of the molds, which suggested they had been in contact with molten metal. Gettens’ (1969) thorough technological analysis of Shang and Zhou bronzes detailed metallurgical techniques in conjunction with the piece-mold method and further confirmed that this was indeed the technique employed by Chinese casters. Bagley’s (1977) analysis of the bronzes then highlighted the relationship between this casting procedure and the design of the bronzes themselves. For example, the seams of the molds were eventually turned into decorated flanges on the bronzes, the designs of the patterns were reliant on sectioned molds which were in turn reliant on the model shape and alignment of the legs was dependent on the mold sections. While there are still facets of the bronze casting process that are still being researched or debated, the overall procedure is relatively well understood thanks to the above researchers and many others in the past ~90 years. Following Li (2022: 47), the operational sequence of bronze casting in Anyang, and presumably elsewhere, can be summarized in nine steps: 1) clay preparation, 2) model making, 3) making mold sections, 4) making cores, 5) metal preparation, 6) furnace building, 7) metal melting, 8) casting, and 9) finishing. Currently little research has clarified the steps around furnace building and firing, but a fair amount of work has been done to elucidate the steps involving clay. The focus of this chapter continues to expand on the clay preparation section of this process, the results of which could influence other parts of the sequence depending on the 188 functional properties of the prepared clay. The aeolian loess of the Central Plains is particularly well-suited to the piece-mold method because of its structural strength and low-shrinkage. Stoltman et al. (2009; 2018)’s petrographic observations of mold thin-sections indicate that further processing was done, likely via levigation, to remove additional clay from the sediment. Their study examined the molds via point-counting thin-sections and compared bulk composition results to local sediment, illustrating that the molds had elevated silt content by comparison. The benefits of removing the clay component enhanced existing benefits of loess by further reducing plasticity and shrinkage, allowing the material to better receive and retain fine decoration and retain its overall form when being sectioned and reassembled. Stoltman et al. (2009) also note that the elevated silt content would increase refractoriness and resistance to cracking, and the reduction in clay increases porosity which aids in the escaping of gas and reduces thermal conductivity. The removal of clay also allows for easy sectioning and carving without fear of warping. Experimental work has also shown that removing clay facilitates carving fine lines directly onto the mold surface (Sacharuk 2014). Debate is ongoing as to whether designs were carved directly into the model, the mold, or both – most recently with Nickel (2006) arguing for the model based on observations of molds from the Western Zhou, Bagley (2009) arguing for the latter based on observations of Shang bronzes, and Yue et al. (2016) arguing primarily for a combination (e.g., impressions from a model, further decorations on the mold surface, and additional applique) based on extensive analysis of molds at the Anyang workstation. In each of these scenarios, however, the importance of the ceramic materials (silty loess with clay 189 removed) cannot be understated in terms of the retention of decorations during carving, the lack of shrinkage, and stability in both the removal and reassembly of pieces for casting. Evidently loess is an important material for bronze-casting in the north, but as discussed in earlier sections, the soil available in Panlongcheng (“Xiashu Loess”) is different from that available in the Central Plains. The deposits may have the same origin but went through further pedogenic processes in the Jianghan Plain that made grain size smaller, more angular and less homogeneous. A reconstruction of the paleoclimate and depositional environment also indicates that the amount of clay and sand would have increased during the early Bronze Age (Wang et al. 2012). Not only that, but systematic coring has revealed that certain areas are high in reticulated laterite and cinnamon soil moved from lakebeds to the cultural horizon (Zhang et al. 2018). Our understanding of the mold-making and bronze-casting process has been shaped by the tens of thousands of molds unearthed at Yinxu, but regional environmental differences would have impacted the mold-making process in Panlongcheng. The following analysis compares the microstructure of mold samples from the Xiaomintun (XMT) foundry in Yinxu to samples from Panlongcheng excavated from M26 in Yangjiazui, quantifying differences in grain- and void-size and morphology. This is combined with the information acquired from the previous chapters to understand the mold recipes within the broader context of pottery production in their respective regions. The aim of this chapter is to explore the similarities and differences of Panlongcheng molds to Yinxu molds, examining whether the different environmental circumstances of 190 Panlongcheng required bronze-casters to adapt their recipe to local sources or whether they were able to maintain a similar recipe using siltier material in the area. Considering the political and economic importance of bronzes and bronze-casting during the Erligang and Yinxu periods, elucidating regional practice can provide insight into the ways in which Panlongcheng residents changed or maintained a technological style for material so connected to the power and ritual of their homeland. 7.1 Analysis 7.1.1 Methodology A total of 6 samples and 8 images were acquired. All images are the same resolution and magnification (250x) to ensure consistency (Table 9; see Appendix C for images). The magnification of 250x was chosen as it was the best field of view to incorporate as many grain sizes as possible (Fine Silt to Very Fine Sand) without compromising quality. Images were only used if taken in a central part of the sample (i.e., sample edges were excluded). Type Site # of Samples # of Images # of Grains (12.5 μm-125 μm) Median # of Silt Grains per Image Mold Panlongcheng 2 3 315 108 Mold Yinxu 4 5 1360 240.5 Total 6 8 1675 225 Table 9. Number of Mold Samples and Images Analyzed. The molds are sourced from Panlongcheng (M26) and Yinxu (Xiaomintun), as molds from Zhengzhou were unavailable for sampling. However, considering how much of our understanding of bronze-casting is based on Anyang molds, this comparison can still 191 provide insight. Of the samples, 4 are from Yinxu and 2 are from Panlongcheng, with ~80-200 data points each. Given the relative rarity of Panlongcheng bronze molds, and the inherently destructive nature of thin-sectioning, the sample size is much more limited in this chapter. The sample from Panlongcheng, however, offers an incredibly unique opportunity for comparison. The sample originates from the interior handle of a bronze found in an elite tomb (M26) in Yangjiazui and is therefore a mold that resulted in a successful cast of an elite ware. Thus, while limited, the quantitative and qualitative comparison of these samples provide an opportunity to investigate any technological differences present, the existence of which could both inform our understanding of Panlongcheng ceramic technology but also greatly impact our understanding of any Panlongcheng bronze casting industry. Similar pre-processing measures were taken as those in the preceding chapters. For each image, the scale was set and removed. A new macro rule was built for these images as they were acquired using a different SEM and are different dimensions compared to the previous chapters (2560x1670 pixels or 509.5x332.3 μm with the scale removed). Slight adjustments to contrast and brightness were made if necessary in a similar, qualitative manner as the previous chapters. 7.1.2 Training and Segmentation Each image was again segmented using the Trainable WEKA Segmetation (TWS) package and used the same pre-trained classifier as the previous analyses. Five classes were identified - voids, three different grain types (aligning with BSE differences in 192 quartz/feldspar, Fe-Mg, and Fe-Ti oxides), and clay matrix. The voids are all black (0) and one of the grain types is all white (255), and the rest of the classes are different grayscale values. Grayscale values varied across images - the relative difference between the grains was more salient to the training process. In some of these images, there are large calcium carbonate inclusions (as per EDX spectral results). These inclusions were approximately within the same grayscale value range as the clay matrix in pottery samples, so they were easily segmented and removed for grain-size analysis. Note that the grain classes were separated during training so as to not confuse the training data, but were ultimately recombined into the same image for analysis. Approximately ~40-50 pixels for each class were selected and the classifier was retained, with iterative training until the segmentation was found suitable by comparing the segmentation results to the “overlay” feature built into the software. The “Get Probability” option was selected, producing a stack of images that could be separated into discrete images for each class. The classes of images that contained grains were added together and thresholded (Image -> Adjust -> Threshold -> Otsu). The image was then despeckled (Process -> Noise -> Despeckle) to remove stray pixels that affected edges; the image was again compared to the original and any remaining errors (like cracks) were corrected manually. The desired measurements were set (1005 pixels per 200 μm, as per the image scale) (Analyze -> Set Measurements - Area, Shape descriptors, Perimeter, Fit ellipse, Feret’s diameter) and the particles analyzed (Analyze -> Analyze Particles). A minimum of 15 μm2 in particle area was set for the same reason as before; anything smaller ran the risk 193 of being artifacts of the thresholding process rather than actual particles. Particles on edges were excluded. The resulting measurements were saved to a spreadsheet with the sample name, image name, excavation site, and magnification. The results of segmentation can be seen in Figure 57. (a) (b) (c) (d) Figure 57. a) Panlongcheng mold b) Yinxu mold c) Panlongcheng mold isolated particles d) Yinxu mold isolated particles. 194 7.1.3 Results - Grains 7.1.3.1 Cumulative Frequency The particle range for the mold samples was restricted to 12.5 to 125 μm (inclusive) based on preliminary visual inspection of the images - anything smaller than 12.5 μm would be too small for analysis and anything larger than 125 μm was too few in number (and/or bisected by image edges) to produce significant comparative results. The mold samples cluster with their respective sites (Figure 58), and when aggregated (Figure 59) the cumulative frequency graph indicates that the difference Figure 58. Cumulative frequency of mold samples. Panlongcheng mold samples have higher quantities of larger grains compared to Zhengzhou mold samples. 195 Figure 59. Cumulative frequency of molds samples by site. between Yinxu and Panlongcheng grain-size distributions (as indicated by different slopes) differs most between 3-6 phi (15-125 μm). Panlongcheng’s samples are more heavily dominated by very fine sand (3-4 phi) at ~30-55% of the grain area, whereas Yinxu samples are about 50% coarse silt (4-5 phi) out of the total grain area. The Yinxu slopes from [3,4)-[4,5) indicate a mode of coarse silt across all samples. Since cumulative frequency is the proportion of grain sizes relative to the total area taken up by grains (rather than image area), we can directly compare the cumulative frequency graphs of the molds to the cumulative frequency graphs of the samples from previous sections. This comparison is restricted to silt grains (12.5 to 63 μm) to directly compare the raw materials rather than any natural or manually added sand. Based on cumulative frequency, the Yinxu mold distribution differs from untempered and li 196 ceramics. The cumulative percentage overall has higher coarse (47%) and medium silt content (41%), while Zhengzhou untempered and li samples have more fine silt (Figure 60). This could be due to the removal of fine content (primarily clay) during the mold-making process, as discussed by Stoltman et al. (2012). The removal of clay (likely from levigation) reduces the amount of shrinkage that occurs, allowing the molds to retain fine, detailed carving directly on its surface (Stoltman et al. 2009; Stoltman et al. 2018). The reduction of finer silt content compared to untempered material could be a byproduct of this process. All types differ greatly from the gang samples excavated at Zhengzhou, which again are suspected to have originated in Panlongcheng based on the lower quantities of coarse silt. Panlongcheng molds differ much more relative to Panlongcheng ceramic material (Figure 61). These molds have much higher coarse silt (~60% of all silt grains, vs. 15% untempered from Panlongcheng), and another 30% being medium silt (vs. ~40% for Panlongcheng untempered pottery). This is the case compared to gang and li from Panlongcheng as well, which all align fairly closely in terms of silt content. These differences are stark even without the inclusion of very fine sand, which is another major component of Panlongcheng molds. 197 Figure 60. Cumulative frequency of central plains (Zhengzhou/Yinxu) ceramics by type. The types are roughly similar in cumulative frequency except for gang, which is likely to have originated in Panlongcheng. 7.1.3.2 Relative Density Relative density is also reflected in the stacked bar chart (Figure 62), showing the average area of each grain class per sample image. With one exception, about 35% of each sample image is comprised of silt and very fine sand grains. For Panlongcheng samples, around 12-20% of the image is of silt (the majority being coarse silt), and the remainder (~10-18%) constitutes very fine sand. By contrast, Yinxu images constitute only 1-3% very fine sand and ~20-34% silt, over half of which is typically coarse silt and ~8-16% medium silt. The high quantity of sand for Panlongcheng samples stands in stark contrast to its pottery, which typically contained minimal quantities within the visible range - even its li samples were typically only ~1% very fine sand. 198 Figure 61. Cumulative frequency of Panlongcheng ceramics by type. The types are roughly similar except for the mold samples, suggesting a different area of production. Figure 62. Average image area percentage of each grain class by site for mold sample. The Panlongcheng mold samples are heavy in very fine sand and coarse silt; Yinxu (Xiaomintun)’s samples are coarse/medium-silt heavy like its other ceramic products. 199 When broken down by specific grain-size bins, Yinxu images are more densely packed with grains from 15-60 μm (i.e., silt grains), whereas Panlongcheng samples are fairly evenly distributed across grain sizes from 15-85 μm (Figure 63). An exception is Sample 09AG16, where the total area and grain distribution is affected by large calcium carbonate inclusions used to bind the molds together (based on finds in Stoltman 2018). As such, the grains drop off around 80 μm which is consistent with the other Yinxu samples, but the percentage of silt in the image is also reduced. As one can see in Figure 62, the relative proportions of the grain sizes is still consistent, however (i.e., the majority of area taken up by grains is Medium and Coarse silt). For Panlongcheng pottery, the grains cluster around 15-20 μm but for molds the grains are more evenly distributed across bin sizes all the way up to 90-120μm. Figure 63. Average image area percentage of grain size per sample (molds). 200 7.1.3.3 Statistical Moments As a reminder, the calculation of statistical moments is used to derive a mean in a geological context (see Chapter 5.3.4.3). The moments here are calculated to compare both silt size (15-63 μm) and the full range of grains analyzed in each image (15-125 μm). The results are presented in Table 10. Excavation Site Type Range Mean St.Dev. Skewness Kurtosis Panlongcheng Mold 12.5 μm-63 μm 39.31 1.29 -2.07 215.16 Panlongcheng Mold 12.5 μm-125 μm 60.69 2.61 1.19 195.26 Yinxu Mold 12.5 μm-63 μm 33.68 1.25 3.19 218.83 Yinxu Mold 12.5 μm-125 μm 36.12 1.54 8.28 385.04 Table 10. Statistical Moments - Feret Diameter (μm) of Grains (15-125 μm) in Mold Samples. The statistical moments indicate that, unlike with pottery, Panlongcheng silt is larger in molds (mean grain size = 39.31 μm) compared to Yinxu (mean grain size = 33.68 μm), reflecting the lower proportion of medium and fine silt in Panlongcheng samples. They both have similar levels of variance (1.29 μm versus 1.25 μm respectively), while Panlongcheng’s distribution is negatively skewed (-2.07) in contrast to Yinxu (3.19). When grains >63 μm are included, Panlongcheng mean grain size increases to 60.69 μm in comparison to 36.12 μm for Yinxu, highlighting the predominance of sand grains in the Panlongcheng samples. The standard deviation for Panlongcheng increases to 2.61 μm, reflecting the varied distribution in Panlongcheng samples in Figure 63, while Yinxu increases only slightly to 1.54 μm. The skewness becomes more positive for both, though far more for Yinxu at 8.28 (1.19 for Panlongcheng). 201 For Yinxu, again, the results are consistent with those calculated in the previous sections - untempered pottery sherds were 33.36 μm, with a standard deviation of 1.26 μm and skewness at 4.13 (Table 11). The very slightly higher mean for molds reflects the reduction in fine silt content, but overall the results indicate the consistency of the loess available in the Central Plains region, available in both Zhengzhou and Yinxu, used for both untempered pottery and for bronze mold-making. On the other hand, these results are not comparable to any of those from Panlongcheng pottery samples, where mean silt size (15-63 μm) was around 26-28 μm across all images. The mean silt size in Panlongcheng mold samples is approximately 11 μm larger than any of the pottery types analyzed, and the distributions for each pottery type were positively skewed unlike the mold samples. Excavation Site Type Range Mean St.Dev. Skewness Kurtosis Panlongcheng Mold 12.5 μm-63 μm 39.31 1.29 -2.07 215.16 Panlongcheng Mold 12.5 μm-125 μm 60.69 2.61 1.19 195.26 Panlongcheng Gang 12.5 μm-63 μm 28.42 1.07 8.69 300.89 Panlongcheng Li 12.5 μm-63 μm 28.19 0.97 8.48 330.27 Panlongcheng Untempered 12.5 μm-63 μm 26.66 0.93 8.34 293.87 Yinxu Mold 12.5 μm-63 μm 33.68 1.25 3.19 218.83 Yinxu Mold 12.5 μm-125 μm 36.12 1.54 8.28 385.04 Zhengzhou Gang 12.5 μm-63 μm 26.96 1.03 12.06 382.84 Zhengzhou Li 12.5 μm-63 μm 36.39 1.35 0.46 177.92 Zhengzhou Untempered 12.5 μm-63 μm 33.36 1.26 4.13 213.76 Table 11. Statistical Moments - Feret Diameter (μm) of Silt Grains (15-63 μm) in All Samples. One must acknowledge the limited sample size, as we are analyzing only two samples (and three images) from Panlongcheng. However, these two samples used vastly 202 different raw materials compared not only to Yinxu but also to the rest of Panlongcheng’s other ceramic technology. 7.1.3.4 Morphological Indicators - Logistic Regression 7.1.3.4.1 Yinxu Molds vs. Panlongcheng Molds Multivariate logistic regression is again used to model the difference between particle size and shape characteristics based on proposed origin. The models below focus on grains between 15-63 μm to clarify whether there is a significant difference in size and shape characteristics of the silt used in the recipes. A model analyzing the relationship between Feret diameter and Site is compared with a model using Feret, Solidity, Circularity, Roundness and Aspect Ratio altogether. The logistic regression model using Feret alone indicates a significant difference between the two sites (p<0.01, AIC=1237.3), with the odds ratio of the site being Xiaomintun equaling 0.97 (Std. Error 0.024) for every one unit of Feret diameter increase. However, the difference between the null deviance (1252.7) and residual deviance (1233.3) is fairly small, indicating that the model could be much improved. Visualizing the model with the predicted probabilities shows the likelihood of a grain being from Xiaomintun (y-axis) decreases with Feret (x-axis) (Figure 64). 203 Figure 64. Predicted probabilities for mold samples - Site ~ Feret. Grains are less likely to be from Yinxu/Xiaomintun as they grow in feret diameter. When running the second model, Feret, Circularity and Aspect Ratio are significant. After removing Solidity and Roundness, only Circularity and Feret remain significant so the model is re-run again with only Feret and Circularity (Feret p<0.01, Circularity <0.01). With these two variables, the model has improved over slightly Feret alone (AIC=1229.4, null deviance = 1252.7, residual deviance = 1223.4). Holding all other factors constant, the odds ratio of Zhengzhou equals 0.97 (Std. Error 0.024) with every one unit increase of Feret diameter and 1.02 (Std. Error 0.020) with every one unit increase of Circularity. That is, it becomes more likely that a particle is from Yinxu when Feret decreases and Circularity increases (Figure 65). Note that calculation of predicted probabilities control the remainder of the variables using their respective means; since the dataset is strongly unbalanced toward Yinxu (i.e., nearly four times as many grains as Panlongcheng, see Table 9), the probabilities on the x-axis of predicting Panlongcheng will skew lower than modeling each variable alone. 204 Figure 65. Predicted probabilities for mold samples - Site ~ Feret + Circularity. Grains are less likely to be from Yinxu/Xiaomintun as they grow in feret diameter and get less circular. 7.1.3.4.2 Panlongcheng Molds vs. Panlongcheng Untempered Pottery Considering the difference in composition compared to Panlongcheng pottery, another logistic regression model compares the size and shape characteristics between these data to better understand the materials used for each ceramic recipe. The models below are limited to grains between 15-63 μm from Panlongcheng molds and Panlongcheng untempered pottery. Analyzing the relationship between Feret diameter and Type is compared with a model using Feret, Solidity, Circularity, Roundness and Aspect Ratio altogether. The logistic regression model using Feret alone indicates a significant difference between the two sites (p<0.01, AIC=3551.8), with the odds ratio of a grain being from a mold equaling 1.09 (Std. Error 0.018) for every one unit of Feret diameter increase. The difference between the null deviance (3895.4) and residual deviance (3547.8) is larger than any model so far but still indicates room for improvement. Visualizing the model with 205 the predicted probabilities shows the likelihood of a grain being from a mold (y-axis) grows with Feret (x-axis) (Figure 66). Figure 66. Predicted probabilities for Panlongcheng ceramics - Type ~ Feret. A grain is more likely to be from a mold as it grows in Feret diameter. Figure 67. Predicted probabilities for Panlongcheng ceramics - Type ~ Feret + Circularity + Solidity + Roundness + Aspect Ratio. A grain is more likely to be from a mold as it grows in 206 Feret diameter, becomes more rounded, longer and less angular, but decreases in circularity. In the second model, all variables are significant (Feret p<0.01, Solidity <0.01, Roundness<0.01, Circularity p<0.01, Aspect Ratio p<0.01) and the model has improved over Feret alone (AIC=3142.4, null deviance = 3895.4, residual deviance = 3130.4). Holding all other factors constant, the odds ratio of mold grains equals 1.02 (Std. Error 0.017) with every one unit increase of Feret diameter and 1.14 (Std. Error 0.034) with every one unit increase of Solidity. For Circularity, the odds ratio of a mold is 0.09 (Std. Error 0.016) for every one unit increase, 1.001 (Std. Error) for every one unit increase of Aspect Ratio, and 1.03 (Std. Error 0.007) for every one unit increase in Roundness. That is, it becomes more likely that a particle is from a mold when Feret, Solidity, Roundness and Aspect Ratio increase and Circularity decreases (Figure 67). Again, since the dataset is strongly unbalanced toward pottery (i.e., nearly twice as many grains as molds, see Table 9), the probabilities on the x-axis of predicting molds will skew lower than modeling each variable alone. 7.1.4 Results - Voids Analyzing the voids of bronze-casting molds will primarily reflect the grain-size and grain-packing characteristics of the molds and the amount of clay matrix that was removed. Unlike pottery, where voids can also reflect firing temperature based primarily on the changes of clay (as seen in Chapter 6), the lack of clay matrix in molds means that firing temperature is not reflected in void size and shape in this analysis. Geodesic diameter is again the primary metric analyzed, which indicates the size and length of a pore. The 207 quantity and size of interconnected pores is representative of effective porosity (the measure of interconnected pore volume relative to the bulk volume). As mentioned in Chapter 5, the interconnectedness of pores can have an impact on permeability. For molds, porosity and permeability impacts the performance characteristics of molds which require the escape of gases and steam during the casting process (Stoltman et al. 2018: 94). The same images as the grain-size analysis were used and the quantity of pores is summarized in Table 12. Type Site # of Voids Median # of Voids per Image Mold Panlongcheng 231 83 Mold Zhengzhou 575 109.5 Total 806 101 Table 12. Number of Voids Analyzed in Mold Samples. 7.1.4.1 Cumulative Frequency The cumulative frequency graph (Figure 68) shows that Yinxu molds have a higher frequency of voids <150 μm, constituting around 75% of total void area versus 55% for Panlongcheng. The slopes above 150 μm are relatively similar, but Panlongcheng has more voids above 350 μm, with a sharp slope at 450 μm. That said, the difference in cumulative frequency is not extreme overall. 208 Figure 68. Cumulative frequency of void geodesic diameter for mold samples. Figure 69. Percentage of image – voids (mold samples). The differences observed are primarily due to larger grains in Panlongcheng samples taking up more area. 209 Figure 70. Average image area percentage of each void geodesic diameter by mold sample. 7.1.4.2 Relative Density While the cumulative frequency graph can show us the proportion of void sizes in relation to the voids themselves, bar charts and histograms showing the proportion of void sizes relative to the image area can give us a better sense of the quantity and density of voids in the sample. Overall, these images indicate that voids take up a larger proportion of the image compared to Panlongcheng samples - around 26-30% of the image versus 20-23% and a notably low 8% for one of the UBC06-101 images (discussed below) (Figure 69). Figure 70 shows there are fewer voids in Panlongcheng samples across all of the size bins 210 outside of two particularly long voids at 350 μm (UBC06-101 Location 2) and 450 μm (UBC06-102) taking up most of the image area. Based on visual inspection, this does seem to be primarily due to the larger grain sizes. The clay matrix seems to have been removed across all samples, but the grain size and packing of Panlongcheng samples does not allow for as many voids as the Yinxu molds. Additionally, in the case of UBC06-101 Location 2, there are large non-mineralogical obstructions filling the channels of voids. 7.2 Discussion These results offer preliminary insights into Panlongcheng bronze casting mold technology, indicating that the two M26 mold samples differ from the more plentiful and better understood molds from Yinxu. They were compared with both Yinxu molds and Panlongcheng pottery in terms of grain and void sizes to understand what specific choices were made when crafting these specific molds. The results indicate that the raw materials used in Panlongcheng molds differ from those at Yinxu, with Panlongcheng molds being heavily composed of very fine sand rather than being almost entirely silt like Yinxu molds. As noted above, the qualities of the Central Plains aeolian loess and its high silt content have been discussed as greatly beneficial to the mold-making process. Its structural strength and low-shrinkage, enhanced even further with the removal of the clay, allowed for ease of carving and the retention of shape when sectioned and re-fitted in pieces. Stoltman et al. (2018: 94) note in their analysis of Yinxu mold microstructure that sand (in the form of temper) was only found in the outer 211 layers of “double-layered molds” and never as part of the inner mold that is decorated. However, Panlongcheng mold-makers appear to have opted for a different recipe for their molds, acquiring raw materials that were far coarser and sandier - albeit with little difference in grain morphology outside of the size, according to the logistic regression model that was run. While this choice could have been to reduce mold shrinkage, the clay matrix was removed in the same manner as Yinxu molds already for this purpose. As noted, this mold was removed from the interior of a successfully cast bronze, indicating that the changes in the paste did not compromise the casting process. The removal of clay matrix is an important feature, as this processing component is consistent with the bronze mold technology from the Central Plains. The removal of clay matrix also implies the presence of another step, the addition of a binder like lime to hold the silt together and burn off during casting without compromising structural integrity via shrinkage (Stoltman et al. 2018). There is therefore a clear connection in the bronze casting chaîne opératoire found in the Central Plains versus this piece in Panlongcheng, but with a clear difference in the microstructural composition of the molds. The raw materials used for these molds differ a great deal even from the Panlongcheng raw materials used for other ceramic pottery. Where Panlongcheng potters seem to have used similar sources for li, gang, and untempered pottery (unlike in Zhengzhou and Yinxu, which both use alluvial clay for their li cooking pots rather than loess), they opted for a totally different source for molds specifically. This seems to be related to where pottery and mold production was located. In Yangjiawan, where all of the pottery sherds were excavated, there is a layer of yellow laterite (“Xiashu loess”) on top of 212 the reticulated red laterite from the Pleistocene era. The so-called “Xiashu loess”, while different from loess available in the Central Plains, does still contain silt, just with lower quantities of coarse grains. The mold samples were found within a bronze handle in an elite tomb (M26) in Yangjiazui, to the west of Yangjiawan, although this obviously does not necessarily reflect the production site. These preliminary results suggest that Panlongcheng mold makers used a different clay source compared to what was available to pottery makers in Yangjiawan and would have had to select or process their clays differently for the purposes of mold making (e.g., they used sandy clays, added sand to low-silt soils, or processed the soils to remove not only clays but also finer silt classes). The reason why this would be the case is still unclear, as mold-makers could have worked in Yangjiawan given the siltiness of soils available in the area. They may have have instead produced this mold in Xiaozui where there is evidence of a bronze-casting workshop, possibly due to convenient transport as proposed by Lu et al. (2020). In Xiaozui, there is the layer of reticulated red laterite and a cultural layer of cinnamon soil. As discussed in Chapter 3 Section 2.4.2., it appears as though Panlongcheng residents manually transported cinnamon soil from nearby lakebeds in Yangjiawan and Xiaozui to a lesser extent (as well as numerous other areas of Panlongcheng) for terraforming purposes. It is difficult at this point to determine which of these two layers were used, but both do differ from the most abundant material at Yangjiawan. 213 7.3 Conclusion While more samples and analyses are required, these preliminary results suggest the existence of a mold-making process in Panlongcheng that maintains aspects of Central Plains piece-molds while integrating local choices. Given the importance of bronze production and circulation of the political economy of the Erligang and late Shang periods, these results are significant for two reasons. First, they provide additional evidence that Panlongcheng had its own, local casting industry, not only for weapons and tools, but also for elite ritual bronzes. This counters previous hypotheses on the existence and role of Panlongcheng as simply a provider of bronze metallurgical materials rather than a producer of bronzes in its own right, and therefore resists the persistent core-periphery model that are often applied to the dynamics between Zhengzhou and Panlongcheng. Although there is still evidently a connection between the two sites, with the maintenance and expressions of power in the form of ritual bronzes, these results require us to consider internal dynamics of power and control within Panlongcheng rather than just power and hierarchy assigned and maintained from the capital city alone. Second, and somewhat relatedly, these results indicate that the bronze production chaîne opératoire is a continuation of the piece-mold process from the Central Plains but with the addition of locally made changes. While the clay matrix was removed as per mold samples from Yinxu, and likely bound together with the use of lime (although this requires further study), the Panlongcheng raw materials are distinctly different from Yinxu molds. Considering the results from Chapter 5, in which Panlongcheng ceramics have large 214 quantities of fine to medium silt, the molds are made with a large very fine sand component, which is curious considering the benefits of silt-based paste for mold-making. As such, we see a new, “hybrid” form of mold that hasn’t been seen before. This change may reflect choices in the location of the bronze production process, or it may reflect material preferences (or both), reflecting specific choices in both cases. Considering the importance of ritual bronzes and bronze casting industries throughout the Shang Dynasty, choosing to change the raw materials, and catalyze an entirely new production process (affecting forming, carving, and casting), is a clear representation of social boundaries being developed alongside the continuation of ritual and connection to the homeland. With the consistency of ritual bronzes between the two sites being cited as proof of their connection, as well as proof of power and control emanating from Zhengzhou, evidence of changes in the underlying production process offers an additional perspective that highlight how multiple social “truths” can be revealed at different levels of analysis. As with the examples discussed in Chapter 2.1, such as Dietler and Herbich’s (1998) study of Luo pottery or Lightfoot et al.’s (1998) analysis of Fort Ross, the results here confirm that typologies are only providing one dimension of understanding when it comes to Panlongcheng identity. Based on these results, Panlongcheng was not just an Erligang colony providing bronze casting materials to the capital. Instead, Panlongcheng appears to have developed a casting method of its own that reflects a uniquely blended, evolving cultural identity. 215 Chapter 8. Conclusion Similar to early studies of the Black Atlantic and its search for “Africanisms” discussed in Chapter 2, the study of Erligang sites has been shaped by the identification of “Erligangisms” and the detailed classifications therein. This has helped trace the spread of Erligang material culture across the Central Plains and out to Shandong Province, to Shanxi and across the Jianghan Plain. However, as with the study of the Black Atlantic, there has become increasing recognition of the complexity and dynamism involved in the dispersal and settlement of people during the Erligang period, particularly with intensified excavations occurring in Panlongcheng in the last decade. The idiosyncrasies of material culture are not simply ‘Erligang-But-Different’, but in fact material representations of mixed identities that simultaneously represent an Erligang origin but also the processes of adaptation, knowledge acquisition and (likely) cultural interaction that occur with a diasporic population. This research provides evidence of these processes from a technological perspective, underscoring the importance of evaluating material culture from different scales of analysis. From certain macro perspectives, such as the formal qualities of bronzes or the consistency of burial practices, Panlongcheng is just an Erligang outpost, albeit an important one, importing and replicating Erligang practices. From the microstructural view, the people of Panlongcheng adapted to new raw materials, completely changed how their fired their pottery, shifted their cooking technology, developed and traded their own pottery types, and even changed their bronze casting mold technology too. Via individual pottery types and in aggregate, the technology of their 216 ceramic assemblage shows a people importing their practices but developing something new along the way, resulting in something “neither here nor there.” Considering the presence of elites, the clear material connections and ongoing interaction with Zhengzhou illustrated by ceramic and bronze exchange, Panlongcheng is the most connected to and ready to duplicate Central Plains culture of all the dispersed sites across the Erligang landscape. Yet despite these strong connections, we see the development of local styles and practices in ceramic production. So what about the sites with little to no elite presence? What can the ceramic technology of sites like Taixi and Daxinzhuang tell us about local adaptation and change? How about sites like Jingnansi, where the majority of ceramics appear to be from local cultures? The discoveries in this dissertation reaffirm Campbell’s (2014a, 2014b) and Li’s (2014) perspectives that ceramic production is a crucial and overlooked line of evidence, and that applying methodologies such as those seen in this dissertation would go a long way in understanding not just Panlongcheng but also the dynamics of smaller Erligang sites. Diaspora, after all, is the scattering and dispersal of people. In alignment with Campbell’s (2014a) identification of Erligang “traditions and variants”, and Dietler and Herbich’s (1998) concept of “micro-styles”, the results here underscore that the study of ceramic technology across the multitude of “Erligang” sites can help us better understand how the throughline of Erligang traits mixes with local variation in stronger and weaker ways depending on the specific cultural, environmental and historical contexts of each site, and how the people across these sites are connected and disconnected from each other all at once. 217 8.2 Summary and Overview of Contributions The study of Panlongcheng and Zhengzhou ceramic technology exemplifies how investigating microstructure and underlying production processes can be used to explore and elucidate the complexity of local histories and identity formation. This perspective is essential for understanding site-specific dynamics and Erligang intra-site interactions, and for refocusing attention toward the actions, activities and experiences of the people inhabiting each site and across sites. The way people engage with the constraints and realities of their contexts both shapes and is shaped by their history. Thinking back to Hall’s poignant remarks on diaspora and identity: cultural identities have histories, and histories “undergo constant transformation” (Hall 1990: 260). Investigating and disentangling technological processes in Erligang sites is essential for reconstructing the histories, historical transformations and identity formation processes of that period. Panlongcheng is one of the best studied Erligang Period sites and this dissertation builds on the decades of research dedicated to the site specifically and that time period more broadly. More intensive excavations in the past decade have especially added substantial information and understanding of the complexities and uniqueness of Panlongcheng and the material culture of its residents, but there are still many areas and avenues left to explore. This research offers one such avenue, providing useful information for ceramics experts on how to further explore the added dimensions of production and resourcing as part of ceramic analyses. The application of digital image analysis can be used to analyze and evaluate the microstructural composition of ceramics from any site. This work has demonstrated the 218 utility of digital image analysis in conjunction with, or as an alternative to, more traditional point-counting methods in order to extract both size and morphological information. This information can be used for statistical comparison and predictive modelling, and to flesh out details of site-specific and/or regional chaîne opératoire. With digital image analysis, we have the opportunity to develop robust comparative analyses using material already available at sites and archaeological storage rooms across China. This research has led to clearer understanding of ceramic production processes in Panlongcheng and the changes either necessitated by geological realities or the result of specific choices, all of which expands our knowledge of Panlongcheng’s ceramic industry and its local identity (see Table 13 for a summary of results from Chapters 5 and 6). We now have clearer information about their raw materials, highlighting differences in raw material acquisition and potential functional impacts, and the way identities can be shaped by environmental constraints. We also have a method of determining a high-fired/low-fired distinction through image analysis, confirming the divergence in firing between Panlongcheng and Zhengzhou and indicating deliberate shifts in pottery production and function. Crucially, both the raw materials and the firing of the li cooking vessels diverged, likely impacting (and/or being impacted by) culturally significant changes in cooking practices. While more information and analysis are needed on the nature and the size of the temper for li from each site, this research indicates that the li from Panlongcheng were less clayey, less densely tempered, and low-fired compared to Zhenghzou li. While the pores are much larger and longer in Zhengzhou li, due to the size and density of the sand 219 temper, the high-fired body may have been able to retain water better than the low-fired body of Panlongcheng li – perhaps making them more efficient for boiling. High-firing with dense tempering also compromises toughness and thermal shock resistance, so Zhengzhou li may have been more prone to breakage, particularly with rapid heating. As such, Panlongcheng li may have been more suitable to cooking at higher temperatures, while Zhengzhou li may have been more suitable for “low and slow” cooking. Again, however, more research is needed, particularly to incorporate vessel form (leg height, wall thickness, surface treatment) and tempering, which will give a more thorough understanding of the functional qualities of the respective li vessels. We can also confirm that gang were transported to the capital city from Panlongcheng, providing evidence that Panlongcheng exported non-elite, southern-style wares to Zhengzhou. Since Zhengzhou had the capabilities to produce gang-like wares, these results suggest that gang were deliberately imported either by Panlongcheng travellers or merchants themselves, or shipped for their unique “exotic” style. Site Silt Grain Size Grain Morphology Firing Temperature Voids Other Panlongcheng ~26-29 µm, strongly positively skewed More irregular, less round Low-fired outside of stoneware and protoporcelain Few micropores present except Da Kou Zun sample - More clayey than Zhengzhou material - Li material the same as other types Zhengzhou ~33-36 µm, less postively skewed Less irregular, more round High-fired Micropores often present, indicator of reduction atmosphere - Gang likely from Panlongcheng - Li material distinctly different from other types Table 13. Summary of Results (Chapters 5 & 6) 220 Given the size and narrowness of most gang bases, it’s difficult to imagine what gang would be used for transport and the logistics of how they would be transported upright. Considering the predominance of rice cultivation in areas south of the Qin Mountains, it’s possible that gang were specifically developed for rice storage, though this is conjecture and would require further analysis in the form of replication and/or materials testing. The confirmation in this dissertation of the movement of goods highlights the role of Panlongcheng not just as a controlled outpost but also as a developer and producer of its own material culture, and opens up some lines of future research for the potential role of gang in Erligang exchange. This research also provides at least a tentative start to investigating differences in bronze molds. Our understanding of the Erligang period, and the Shang Dynasty in general, has been heavily shaped by the study of bronze vessels and their typologies, movements and symbolic significance. Moreover, our understanding of bronze casting is influenced by the overwhelming number of bronze molds unearthed at Yinxu. The analysis of a rare, but crucial, sample found in a bronze at Panlongcheng’s M26 tomb suggests that bronze casting technology may have been different at Panlongcheng. This information has the potential to influence the way we understand bronze casting technology in general and the way that we understand bronze production in the Erligang period. Finally, this dissertation serves as a case study for further exploration into underlying ceramic technologies across other Erligang sites, highlighting the likelihood of local and regional variability in Erligang ceramic technology and production. As we collect more data from more sites, we can elucidate information on the diversity of ceramic practices 221 developed by different people experiencing transformative local histories all while being connected by a shared motherland. 8.3 Future Directions The research in this dissertation does have some limitations that require development for future work. The relative simplicity of digital image analysis as presented limits mineralogical understanding compared to more traditional forms of petrographic analysis, which then limits the extent to which comparisons can be made. Thus, it may be difficult to differentiate between different sites in the Jianghan Plain, for example, due to the amount of laterites available, unless the sites also have other unique soil characteristics to differentiate them. That said, the methodology laid out in this dissertation illustrates what even a relatively straightforward approach can glean; with more sophisticated algorithms and Computer Vision (i.e., the analysis of images via machine learning and neural networks) becoming increasingly more accessible, further elaborations on the analysis of thin sections stand to impart even more information on ancient ceramic technology. Nevertheless, this research has illustrated the potential of digital image analysis of ceramic technology to illuminate macro and microscale processes of continuity and change during the Shang Dynasty. Considering the relative speed and accessibility of software, the primary hurdles would be the cost of thin sectioning, availability of a scanning electron microscope, and the asymmetry of available samples across sites, types and time periods. 222 Another issue in this study is the limited number of thin-section samples. We need more samples across sites and types, particularly of molds. Additional samples from earlier periods of Panlongcheng and Zhengzhou would also be beneficial in developing a more robust synchronic analysis, clarifying the ways in which ceramic technology at both sites changed over time and whether there are any clear periods of divergence as Panlongcheng become more settled as its own identity. Finally, the comparison of samples across sites, particularly those in distinctly different environmental contexts (e.g., Taixi, Sanxingdui and Wucheng), would help further develop baselines of comparison across the landscape. This can help us better understand the processes of habitus and technological style, and the exchange of both goods and ideas, across Shang sites. There are numerous other opportunities for digital image analysis in Erligang research. For example, it would be beneficial to analyze the microstructural heterogeneity of samples via Voronoi maps (e.g. Heijman et al. 2002), particularly in terms of changes over time (including Erlitou remains from Phases 1 & 2), to see whether ceramic technology at Panlongcheng echoes urbanization processes noted by Jing et al. (2013), Hou (2024) and Sun (2017) across the Shang Dynasty. Another possibility would be to focus on sand grain morphology, as temper shape can have an impact on strength and toughness, particularly at high firing temperatures (Müller et al. 2010). The analysis of raw materials and fired tiles from different areas of Panlongcheng and Zhengzhou would also be useful to establish clearer baselines in microstructure and different material sources within sites. Additionally, it would be beneficial to clarify the calcareousness of local materials at Panlongcheng and Zhengzhou since this has a clear 223 impact on the development of microstructure, particularly in the pre-vitrification stage of firing. The results in this dissertation also provide new opportunities for experimental and replication research for Bronze Age China. Now that we have some understanding of the ways in which ceramic technology differed between Panlongcheng and Zhengzhou, experimental research can offer some insight into the practical, hands-on differences and potential answers as to why certain choices were made in light of other alternatives. It would be useful to compare functional qualities between similar vessel types across sites, especially when it comes to gang and other storage vessels in both Zhengzhou and Panlongcheng (like guan). For example, biaxial strength testing on body sherds (Neupert 1994) could be used to better understand whether gang have any storage or transportation benefits compared to other Erligang period vessels. Gang are tempered, despite having no clear use-wear patterns to indicate exposure to heat, which suggest a potential technological choice linked to some other performance characteristic(s). Comparing strength could help clarify whether there is an appreciable difference in function that makes gang particularly suitable for transporting goods. Understanding the strength of gang sherds can also help clarify the logistics around transporting gang such long distances, given their size and shape. When it comes to replication experiments, of particular interest is the li, which are made using different soil types, tempering and firing temperatures at Panlongcheng - all of which have an impact on various performance characteristics and most certainly have an impact on the cooking aspect as well. As mentioned, one of the major differences between 224 li at the two sites could be heating effectiveness and resistance. To pursue this line of inquiry, one could craft li from local materials at both Panlongcheng and Zhengzhou (with size, shape and surface treatments in mind) and heat them over a controlled heat source (e.g. a portable gas burner) with consistent water quantities and measure the amount of water that remains after specific intervals. Even something as simple as differences in water retention, porosity and heating effectiveness can be illuminating with regard to foodways, cooking technology and cooking practices in different cultures. Rebuilding the entire chaîne opératoire of the li at the two sites would be a challenging undertaking, let alone testing the cooking aspect, but the results presented in this thesis help narrow certain possibilities and highlight functional differences in practice. For gang, the tempering of low-fired material, which would have increased the size and number of pores, could have aided in water loss to allow for evaporation and drying of foodstuffs like rice. Testing evaporation rates in controlled temperature and humidity settings between tempered and non-tempered ceramics made with Panlongcheng soils may be another line of inquiry. Lastly, a comparison between mold-making and carving using a variety of materials across Panlongcheng and Yinxu would be incredibly helpful in understanding how much the larger grain-size impacts the aspects of mold-making typically said to have benefitted from the loess in Yinxu. Experimental work can provide opportunities to clarify some of the questions around carving and casting or highlight other issues that are less clear from visual inspection and microstructural analysis alone. 225 Despite the limitations, this research serves as a proof of concept for the application of digital image analysis to investigate ceramic technology. The information uncovered about habitus, practice and technological style that is only partially represented by typological analyses opens up numerous possibilities for future research. The information here provides an additional perspective on how Panlongcheng residents developed new ceramic practices that reflect new local identities, and offers an opportunity to further explore the ceramic technology at dispersed Erligang Period sites. Gathering this information would help us understand more about the various places and contexts where people settled, remembered their homeland but made new homes, and transformed their histories one pot at a time. 226 References Abbots, Emma-Jayne. 2016. “Approaches to Food and Migration: Rootedness, Being and Belonging.” In The Handbook of Food and Anthropology. Edited by Jakob Klein and James Watson, 115-133. Bloomsbury Academic, New York. Åkesson, Urban. 2001. “Numerical Description of Rock Texture by Using Image Analysis and Quantitative Microscopy: Alternative Method for the Assessment of the Mechanical Properties of Rock Aggregates.” PhD Thesis, University of Gothenburg. Aprile, Anna, Giovanna Castellano, and Giacomo Eramo. 2014. “Combining Image Analysis and Modular Neural Networks for Classification of Mineral Inclusions and Pores in Archaeological Potsherds.” Journal of Archaeological Science 50:262–72. Aronson, Meredith, James M Skibo, and Miriam T Stark. 1994. “Production and Use Technologies in Kalinga Pottery.” In Kalinga Ethnoarchaeology: Expanding Archaeological Method and Theory. Edited by W.A. Longacre and J.M. Skibo, 83–111. Smithsonian Institution Press, Washington, D.C. Avieli, Nir. 2016. “Local Foods, Local Specialties, and Local Identity.” In The Handbook of Food and Anthropology. Edited by Jakob Klein and James Watson,144–66. Bloomsbury Publishing. Bagley, Robert W. 1977. “P’an-Lung-Ch’eng: A Shang City in Hupei.” Artibus Asiae 39 (3/4): 165–219. ———. 2009. “Anyang Mold-Making and the Decorated Model.” Artibus Asiae 69 (1): 39–90. ———. 2014. “Erligang Bronzes and the Discovery of the Erligang Culture.” In Art and Archaeology of the Erligang Civilization. Edited by Kyle Steinke. 19–48. Princeton University Press, Princeton. Barceló, JA, A Vila, and J Gibaja. 1996. “An Application of Neural Networks to Use-Wear Analysis. Some Preliminary Results.” In Computer Applications and Quantitative Methods in Archaeology (BAR International Series 845). Edited by Lockyear, K., T.J.T. Sly and V. Mihăilescu-Bîrliba, 63-70. Archaeopress, Oxford. Beckett, Ronald G. 2014. “Paleoimaging: A Review of Applications and Challenges.” Forensic Science, Medicine, and Pathology 10 (3): 423–36. ———. 2017. “Digital Data Recording and Interpretational Standards in Mummy Science.” International Journal of Paleopathology 19:135–41. 227 Beckett, Ronald G, and Gerald J Conlogue. 2009. Paleoimaging: Field Applications for Cultural Remains and Artifacts. CRC Press. Binford, Lewis R. 1965. “Archaeological Systematics and the Study of Culture Process.” American Antiquity 31: 203–10. Blanco-González, Antonio, Attila Kreiter, Kamal Badreshany, John Chapman, and Péter Pánczél. 2014. “Matching Sherds to Vessels through Ceramic Petrography: An Early Neolithic Iberian Case Study.” Journal of Archaeological Science 50:139–52. Bland, CA, AL Roberts, RS Popelka-Filcoff, and CM Santoro. 2017. “Early Vitrification Stages Identified in Prehistoric Earthenware Ceramics from Northern Chile via SEM.” Journal of Archaeological Science: Reports 16:309–15. Blaschke, Thomas. 2010. “Object Based Image Analysis for Remote Sensing.” ISPRS Journal of Photogrammetry and Remote Sensing 65 (1): 2–16. Boggs, Sam and others. 2006. Principles of Sedimentology and Stratigraphy. Cambridge University Press. Bouchez, R, J Michael D Coey, Aymé Cornu, Jean Deshayes, J Ladrière, and Romain Coussement. 1979. “Le Magnétisme Des Céramiques Anciennes.” ArchéoSciences, Revue d’Archéométrie 3 (1): 3–22. Bourdieu, Pierre.1977. Outline of a Theory of Practice. Translated by R. Nice. Cambridge University Press. Burger, Wilhelm, and Mark J Burge. 2010. Principles of Digital Image Processing: Fundamental Techniques. Springer. Burnside, CD, AS Walker, JN Hampton, and G Soffe. 1983. “A Digital Single Photograph Technique for Archaeological Mapping and Its Application to Map Revision.” The Photogrammetric Record 11 (61): 59–68. Butler, Judith, and Gender Trouble. 1990. Gender Trouble: Feminism and the Subversion of Identity. Routledge Press. CAA Online Proceedings. https://proceedings.caaconference.org/. Accessed January 20, 2020. Campbell, Roderick. 2014a. Archaeology of the Chinese Bronze Age: From Erlitou to Anyang. Cotsen Institute of Archaeology Press. 228 Campbell, Roderick. 2014b. “Erligang: A Tale of Two ‘Civilizations’.” In Art and Archaeology of the Erligang Civilization. Edited by Kyle Steinke, 121–35. Princeton University Press. Campbell, Roderick, Yitzchak Jaffe, Christopher Kim, Camilla Sturm, and Li Jaang. 2022. “Chinese Bronze Age Political Economies: A Complex Polity Provisioning Approach.” Journal of Archaeological Research 30:1–48. Chang, Kwang-chih. 1989. “Ancient China and Its Anthropological Significance.” In Archaeological Thought in America. Edited by C.C. Lamberg-Karlovsky, 155–66. Cambridge University Press. Chen, Hui. 2018. "Brief Report on the excavation of Yangjiawan in Panlongcheng, Wuhan in 2014 武汉市盘龙城遗址杨家湾 2014 年发掘简报." Kaogu考古 11 (2018): 15-33. Chen, Hui and Jing Zhichun. 2020. “Preliminary Observations on Pottery Production Techniques of Pottery Unearthed at Panlongcheng 盘龙城遗址出土陶器制作技术初步观察”. Jianghan Archaeology 江汉考古 6:116-125. Cipolla, Craig N. 2014. Native American Diaspora and Ethnogenesis. Oxford Handbooks Online. Cohen, Abner. 2018. “Cultural Strategies in the Organization of Trading Diasporas.” In The Development of Indigenous Trade and Markets in West Africa. Edited by C. Meillassoux, 266–81. Routledge. Cohen, R. 1997. Global Diasporas. UCL Press. Cowley, David C. 2012. “In with the New, out with the Old? Auto-Extraction for Remote Sensing Archaeology.” Remote Sensing of the Ocean, Sea Ice, Coastal Waters, and Large Water Regions 8532:37–45. Cultrone, Giuseppe, Carlos Rodriguez-Navarro, Eduardo Sebastian, Olga Cazalla, and Maria Jose De La Torre. 2001. “Carbonate and Silicate Phase Reactions during Ceramic Firing.” European Journal of Mineralogy-Stuttgart 13 (3): 621–34. Cusick, James G. 2000. “Creolization and the Borderlands.” Historical Archaeology 34(3):46–55. Dal Sasso, Gregorio, Lara Maritan, Sandro Salvatori, Claudio Mazzoli, and Gilberto Artioli. 2014. “Discriminating Pottery Production by Image Analysis: A Case Study of Mesolithic and Neolithic Pottery from Al Khiday (Khartoum, Sudan).” Journal of Archaeological Science 46:125–43. 229 De Laet, Véronique, Etienne Paulissen, and Marc Waelkens. 2007. “Methods for the Extraction of Archaeological Features from Very High-Resolution Ikonos-2 Remote Sensing Imagery, Hisar (Southwest Turkey).” Journal of Archaeological Science 34 (5): 830–41. Deagan, Kathleen. 2013. “Hybridity, Identity, and Archaeological Practice.” In The Archaeology of Hybrid Material Culture. Edited by Jeb Card, 260-276. SIU Press. Dietler, Michael, and Ingrid Herbich. 1998. “Habitus, Techniques, Style: An Integrated Approach to the Social Understanding of Material Culture and Boundaries.” In The Archaeology of Social Boundaries. Edited by Miriam Stark, 232-263. Smithsonian Institution Press. Dobres, Marcia-Anne. 2000. Technology and Social Agency: Outlining a Practice Framework for Archaeology. Blackwell Publishers. Dorsett, J.E., D.D. Gilbertson, C.O. Hunt, and G.W.W. Barker. 1984. “The UNESCO Libyan Valleys Survey VIII: Image Analysis of Landsat Satellite Data for Archaeological and Environmental Surveys.” Libyan Studies 15:71–80. Dufoix, Stéphane. 2018. “Diaspora before It Became a Concept.” In Routledge Handbook of Diaspora Studies. Edited by Robin Cohen and Carolin Fischer, 13–21. Routledge. Durham, Peter, Paul Lewis, and Stephen J Shennan. 1996. “Image Processing Strategies for Artefact Classification.” In Interfacing the Past: Computer Applications and Quantitative Methods in Archaeology. Edited by H. Kamermans and K. Fennema, 235-240. University of Leiden. Edwin, Romy Suryaningrat, Mushthofa Mushthofa, Elke Gruyaert, and Nele De Belie. 2019. “Quantitative Analysis on Porosity of Reactive Powder Concrete Based on Automated Analysis of Back-Scattered-Electron Images.” Cement and Concrete Composites 96:1–10. Eto, Moriharu. 1963. “Firing Temperature of Jomon Pottery.” Journal of the Anthropological Society of Nippon 71 (1): 23–51. Finch, Jonathan. 2013. “Inside the Pot House: Diaspora, Identity, and Locale in Barbadian Ceramics.” Journal of African Diaspora Archaeology and Heritage 2 (2): 115–30. Fischler, Claude. 1988. “Food, Self and Identity.” Social Science Information 27 (2): 275–92. Fitzpatrick, Scott M, William R Dickinson, and Geoffrey Clark. 2003. “Ceramic Petrography and Cultural Interaction in Palau, Micronesia.” Journal of Archaeological Science 30 230 (9): 1175–84. Frankel, David. 2000. “Migration and Ethnicity in Prehistoric Cyprus: Technology as Habitus.” European Journal of Archaeology 3 (2): 167–87. Franklin, Ursula. 1999. The Real World of Technology. House of Anansi. Freitas, Renato P, Filipe A Coelho, Valter S Felix, Marcelo O Pereira, Marcos André Torres de Souza, and Marcelino J Anjos. 2018. “Analysis of 19th Century Ceramic Fragments Excavated from Pirenópolis (Goiás, Brazil) Using FT-IR, Raman, XRF and SEM.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 193:432–39. Friedman, Gerald M. 1958. “Determination of Sieve-Size Distribution from Thin-Section Data for Sedimentary Petrological Studies.” The Journal of Geology 66 (4): 394–416. ———. 1961. “Distinction between Dune, Beach, and River Sands from Their Textural Characteristics.” Journal of Sedimentary Research 31 (4): 514–29. ———. 1962. “On Sorting, Sorting Coefficients, and the Lognormality of the Grain-Size Distribution of Sandstones.” The Journal of Geology 70 (6): 737–53. Gartley, Richard T. 1979. “Afro-Cruzan Pottery: A New Style of Colonial Earthenware from St. Croix.” Journal of the Virgin Islands Archaeological Society 8:47–61. Giddens, Anthony. 1984. The Constitution of Society: Outline of the Theory of Structuration. Harvard University Press. Gilroy, Paul. 1993. The Black Atlantic: Modernity and Double Consciousness. Harvard University Press. Gosden, Chris, and Chantal Knowles. 2020. Collecting Colonialism: Material Culture and Colonial Change. Routledge. Gosselain, Olivier P. 1992. “Bonfire of the Enquiries. Pottery Firing Temperatures in Archaeology: What For?” Journal of Archaeological Science 19 (3): 243–59. Gruen, Armin, and Emmanuel P. Baltsavias. 1990. “Close-Range Photogrammetry Meets Machine Vision.” Society of Photo-Optical Instrumentation Engineers (SPIE) Proceedings 1395. Hall, Stuart. 1990. “Cultural Identity and Diaspora”. In Identity: Community, Culture, Difference. Edited by J. Rutherford, 222-237. Lawrence and Wishart. 231 Harris, Oliver JT. 2014. “(Re) Assembling Communities.” Journal of Archaeological Method and Theory 21(1): 76–97. Hastorf, Christine A. 2012. “The Habitus of Cooking Practices at Neolithic Çatalhöyük.” In The Menial Art of Cooking: Archaeological Studies of Cooking and Food Preparation. Edited by Sarah Graff and Enrique Rodriguez-Alegria, 47-65. University Press of Colorado. Heath, Barbara J. 1999. “Yabbas, Monkeys, Jugs, and Jars: A Historical Context for African Caribbean Potterys on St Eustatius.” In African Sites Archaeology in the Caribbean. Edited by Jay B. Haviser, 196–220. Markus Weiner. Hegmon, Michelle. 1998. “Technology, Style, and Social Practices: Archaeological Approaches.” In The Archaeology of Social Boundaries. Edited by Miriam Stark, 232–63. Smithsonian Institution Press. Heijman, M. J. G. W., N. E. Benes, J. E. ten Elshof, and H. Verweij. 2002. "Quantitative Analysis of the Microstructural Homogeneity of Zirconia-Toughened Alumina Composites." Materials Research Bulletin 37 (1): 141-149. Heilbronner, Renée, and Steve Barrett. 2013. Image analysis in earth sciences: microstructures and textures of earth materials. Vol. 129. Springer Science & Business Media. Hein, Anke. 2016. “The Problem of Typology in Chinese Archaeology.” Early China 39:21–52. Hein, Irmgard, Alfonso Rojas-Domínguez, Manuel Ornelas, Giulia D’Ercole, and Lisa Peloschek. 2018. “Automated Classification of Archaeological Ceramic Materials by Means of Texture Measures.” Journal of Archaeological Science: Reports 21:921–28. Henansheng Wenwu Kaogu Yanjiusuo. 2001. A Report of Excavations at Zhengzhou Shangcheng from 1953 to 1985 Vol.1 (郑州商城——1953~ 1985 年考古发掘报 Vol.1) Wenwu Chuban She文物出版社. Hong, Hanlie, Zhaohui Li, and Ping Xiao. 2009. “Clay Mineralogy along the Laterite Profile in Hubei, South China: Mineral Evolution and Evidence for Eolian Origin.” Clays and Clay Minerals 57 (5): 602–15. Hou, Weidong. 2024. Archaeological Research on Zhengzhou Shangcheng and Wangji Regional Settlement 郑州商城与王畿区域聚落考古研究. Chinese Social Sciences Postdoctoral Library. 232 Hu, Di. 2013. “Approaches to the Archaeology of Ethnogenesis: Past and Emergent Perspectives.” Journal of Archaeological Research 21:371–402. Huang, Chun Chang, Jiangli Pang, Shu’e Chen, and Zhanping Zhang. 2003. “Holocene Dust Accumulation and the Formation of Polycyclic Cinnamon Soils (Luvisols) in the Chinese Loess Plateau.” Earth Surface Processes and Landforms: The Journal of the British Geomorphological Research Group 28 (12): 1259–70. Hubei Provincial Museum. 1976. “Excavation of Shang Dynasty Ruins in Panlongcheng, Huangpi, Hubei in 1963 一九六三年湖北黄陂盘龙 城商代遗址的发掘.” Wenwu 1: 49-59+100-101. Ionescu, Corina, Volker Hoeck, and Lucreţia Ghergari. 2011. “Electron Microprobe Analysis of Ancient Ceramics: A Case Study from Romania.” Applied Clay Science 53 (3): 466–75. Ionescu, Corina, Volker Hoeck, and others. 2011. “Firing-Induced Transformations in Copper Age Ceramics from NE Romania.” European Journal of Mineralogy 23 (6): 937–58. Jahne, Bernd. 2004. Practical Handbook on Image Processing for Scientific and Technical Applications. CRC press. Jain, Anil K. 1989. Fundamentals of Digital Image Processing. Prentice Hall. Jing, Zhichun, Tang Jigen, George Rapp, and James Stoltman. 2013. “Recent Discoveries and Some Thoughts on Early Urbanization at Anyang.” A Companion to Chinese Archaeology. Edited by Anne Underhill, 343–66. Josa, V Galván, SR Bertolino, JA Riveros, and G Castellano. 2009. “Methodology for Processing Backscattered Electron Images. Application to Aguada Archaeological Paints.” Micron 40 (8): 793–99. Karlbeck, Orvar. 1935. “Anyang Moulds.” Bulletin of the Museum of Far Eastern Antiquities 7: 39–60. Keightley, David N. 1978. “The Late Shang State: When, Where, and What?” In The Origins of Chinese Civilization. Edited by David N. Keightley, 523-64. University of California Press. Kerr, Rose and Nigel Wood. 2004. Science and Civilisation in China: Volume 5, Chemistry and Chemical Technology, Part 12, Ceramic Technology. Cambridge University Press. 233 Kondo, Seiji, Kawashima, Chihiro, and Tanahashi, Toshikazu. 1935. “On Jomon Pottery (Part 1) Chemical Composition and Physical Properties. 縄紋土器に就て (第 1 報) 化學組成と物理性.” Journal of the Great Japan Ceramic Association 大日本窯業協會雑誌 43 (512): 481–88. Krinsley, David H, Kenneth Pye, Sam Boggs Jr, and N Keith Tovey. 1998. Backscattered Scanning Electron Microscopy and Image Analysis of Sediments and Sedimentary Rocks. Cambridge University Press. Krumbein, WC, and FJ Pettijohn. 1938. Manual of Sedimentary Petrography. D. Appleton-Century Company. Lemonnier, Pierre. 1986. “The Study of Material Culture Today: Toward an Anthropology of Technical Systems.” Journal of Anthropological Archaeology 5 (2): 147–86. Li, Wenjing, Wugan Luo, Fengchun Huang, Yue Chen, and Changsui Wang. 2023. “Integrating Typological and Stylistic Characteristics with Scientific Results on the Provenance Study of of Proto-Porcelain from the Yejiashan Cemetery Dating to the Early Western Zhou Dynasty.” Heritage Science 11 (1): 229. Li, Yung-ti. 2014. “The Politics of Maps, Pottery, and Archaeology: Hidden Assumptions in Chinese Bronze Age Archaeology.” In Art and Archaeology of the Erligang Civilization. Edited by Kyle Steinke, 137-146. Princeton University Press. ——— 2019. Kingly Crafts: The Archaeology of Craft Production in Late Shang China. Columbia University Press. Lightfoot, Kent G, Antoinette Martinez, and Ann M Schiff. 1998. “Daily Practice and Material Culture in Pluralistic Social Settings: An Archaeological Study of Culture Change and Persistence from Fort Ross, California.” American Antiquity 63 (2): 199–222. Lilley, Ian. 2004. “Diaspora and Identity in Archaeology: Moving beyond the Black Atlantic.” In A Companion to Social Archaeology. Edited by Lynn Meskell and Robert W. Preucel, 287–312. Blackwell Publishers. ———. 2006. “Archaeology, Diaspora and Decolonization.” Journal of Social Archaeology 6 (1): 28–47. Liu, Li, and Xingcan Chen. 2001. “Cities and Towns: The Control of Natural Resources in Early States, China.” Bulletin-Museum of Far Eastern Antiquities (73): 5–47. ———. 2012. “Formation of Early States in the Central Plain: Erlitou and Erligang (1900/1800-1250 BC).” The Archaeology of China: From the Late Paleolithic to the 234 Early Bronze Age, 253–96. Cambridge University Press. Liu, Ruiliang, A. Mark Pollard, Cheng Liu, and Jessica Rawson. 2020. “Every Cloud Has a Silver Lining: Using Silver Concentration to Identify the Number of Sources of Lead Used in Shang Dynasty Bronzes.” Acta Geologica Sinica-English Edition 94 (3): 585–93. Liu, Ruiliang, A. Mark Pollard, Jessica Rawson, Xiaojia Tang, Peter Bray, and Changping Zhang. 2019. “Panlongcheng, Zhengzhou and the Movement of Metal in Early Bronze Age China.” Journal of World Prehistory 32:393–428. Livingood, Patrick C, and Ann S Cordell. 2009. “Point/Counter Point: The Accuracy and Feasibility of Digital Image Techniques in the Analysis of Ceramic Thin Sections.” Journal of Archaeological Science 36 (3): 867–72. Lormand, Charline, Georg F Zellmer, Károly Németh, Geoff Kilgour, Stuart Mead, Alan S Palmer, Naoya Sakamoto, Hisayoshi Yurimoto, and Anja Moebis. 2018. “Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks.” Microscopy and Microanalysis 24 (6): 667–75. Lu, Jindong. 2020. “Regarding Copper Casting Remains Discovered in Panlongcheng and Related Issues 论盘龙城发现的铸铜遗存及相关问题.” Jianghan Archaeology 江汉考古 6: 67-75. Lyu, Kai, Wei She, Changwen Miao, Honglei Chang, and Yue Gu. 2019. “Quantitative Characterization of Pore Morphology in Hardened Cement Paste via SEM-BSE Image Analysis.” Construction and Building Materials 202:589–602. Mahapatra, Dwarikanath. 2014. “Analyzing Training Information from Random Forests for Improved Image Segmentation.” IEEE Transactions on Image Processing 23 (4): 1504–12. Malouchos, Elizabeth Watts. 2020. “Angel Ethnogenesis and the Cahokian Diaspora.” In Cahokian Dispersions: Diasporic Connections in the Mississippian Southeast. Edited by Melissa R. Baltus, Sarah E. Baires, Elizabeth Watts Malouchos and Jayur Madhusudan Mehta, 128–56. Springer. Maniatis, Yannis, and Michael S Tite. 1981. “Technological Examination of Neolithic-Bronze Age Pottery from Central and Southeast Europe and from the Near East.” Journal of Archaeological Science 8 (1): 59–76. Matson, Frederick R. 1939. Further Technological Notes on the Potter of the Younge Site, Lapeer County, Michigan. Michigan Academy of Science, Arts and Letters. 235 ———. 1970. “Some Aspects of Ceramic Technology.” In Science in Archaeology. A Survey of Progress and Research, 592–602. Praeger. ———. 1981. “Archaeological Ceramics and the Physical Sciences: Problem Definition and Results.” Journal of Field Archaeology 8 (4): 448–56. Miksa, Elizabeth, and James M Heidke. 1995. “Drawing a Line in the Sands: Models of Ceramic Temper Provenance.” The Roosevelt Community Development Study 2:133–205. Mirti, Piero, and Patrizia Davit. 2004. “New Developments in the Study of Ancient Pottery by Colour Measurement.” Journal of Archaeological Science 31 (6): 741–51. Muller-Lyer, FC. 1889. “Optische Urteilstauschungen.” Archiv Fur Anatomie Und Physiologie, Physiologische Abteilung 2:263–70. Müller, Noémi S., Vassilis Kilikoglou, Peter M. Day, and George Vekinis. 2010. "The Influence of Temper Shape on the Mechanical Properties of Archaeological Ceramics." Journal of the European Ceramic Society 30 (12): 2457-2465. Neupert, Mark A. 1994. "Strength Testing Archaeological Ceramics: A New Perspective." American Antiquity 59 (4): 709-723. Nickel, Lukas. 2006. “Imperfect Symmetry: Re-Thinking Bronze Casting Technology in Ancient China.” Artibus Asiae 66 (1): 5–39. O’Brien, Michael J, John L Beets, Robert E Warren, Tachpong Hotrabhavananda, Terry W Barney, and Eric E Voigt. 1982. “Digital Enhancement and Grey-Level Slicing of Aerial Photographs: Techniques for Archaeological Analysis of Intrasite Variability.” World Archaeology 14 (2): 173–90. Ohnuki-Tierney, Emiko. 1994. Rice as Self: Japanese Identities through Time. Princeton University Press. Otsu, Nobuyuki. 1975. “A Threshold Selection Method from Gray-Level Histograms.” Automatica 11 (285–296): 23–27. Parsons, Timothy A. 2012. “Modeling Late Copper Age Demographics on the Great Hungarian Plain Using Ceramic Petrography.” Journal of Archaeological Science 39 (2): 458–66. Peng, Zicheng, Yonggang Liu, Shizhong Liu, Jueming Hua. 1999. “A preliminary investigation on Shang dynasty bronzes and their copper-lead ore sources in Jiangxi, 236 Hubei and Henan 赣鄂豫地区商代青铜器和部分铜铅矿料来源的初探.” Studies in the History of Natural Sciences 自然科学史研究 18 (3): 241–49. Poppe, Lawrence J, AH Eliason, and ME Hastings. 2004. “A Visual Basic Program to Generate Sediment Grain-Size Statistics and to Extrapolate Particle Distributions.” Computers & Geosciences 30 (7): 791–95. Quinn, Patrick Sean. 2022. Thin Section Petrography, Geochemistry and Scanning Electron Microscopy of Archaeological Ceramics. Archaeopress. Rapp, George R. 2009. Archaeomineralogy. Springer. Rasmussen, Kaare Lund, A Guillermo, Andrew D Bond, Karsten Korsholm Mathiesen, and Sergio D Vera. 2012. “Pottery Firing Temperatures: A New Method for Determining the Firing Temperature of Ceramics and Burnt Clay.” Journal of Archaeological Science 39 (6): 1705–16. Reedy, Chandra L, Jenifer Anderson, and Terry J Reedy. 2017. “Quantitative Porosity Studies of Archaeological Ceramics by Petrographic Image Analysis.” MRS Online Proceedings Library (OPL) 1656:337–53. Reedy, Chandra L, Jenifer Anderson, Terry J Reedy, and Yimeng Liu. 2014. “Image Analysis in Quantitative Particle Studies of Archaeological Ceramic Thin Sections.” Advances in Archaeological Practice 2 (4): 252–68. Reedy, Chandra L, and Sachin Kamboj. 2003. “Comparing Comprehensive Image Analysis Packages: Research with Stone and Ceramic Thin Sections.” Development of Web-Accessible Reference Library of Deterred Fibers Using Digital Imaging and Image Analysis. Harpers Ferry: National Park Service, 159–66. Reinhart, Katrinka and Michael D. Edge. 2020. "Multivariate Analysis of Cooking Pots from Elite and “commoner” Contexts at Yanshi Shangcheng: Implications for the Politics of Daily Life and Development of Ceramic Technology in the Early Bronze Age of Northern China." Archaeological and Anthropological Sciences 12 (9):1-26. Ren, Xiaolin, Junjie Xu, Hui Wang, Michael Storozum, Peng Lu, Duowen Mo, Tuoyu Li, Jianguo Xiong, and Tristram R Kidder. 2021. “Holocene Fluctuations in Vegetation and Human Population Demonstrate Social Resilience in the Prehistory of the Central Plains of China.” Environmental Research Letters 16 (5): 055030. Rice, Prudence M. 1998. “Contexts of Contact and Change: Peripheries, Frontiers, and Boundaries.” Studies in Culture Contact: Interaction, Culture Change, and Archaeology. Edited by James G. Cusick, 44–66. 237 ———. 1987. Pottery Analysis: A Sourcebook. University of Chicago press. Rockman, Marcy. 2003. “Knowledge and Learning in the Archaeology of Colonization.” In The Colonization of Unfamiliar Landscapes, edited by Marcy Rockman and James Steele, 27–43. Routledge. Rye, Owen S. 1981. Pottery Technology: Principles and Reconstruction. Taraxacum. Safran, William. 1991. “Diasporas in Modern Societies: Myths of Homeland and Return.” Diaspora: A Journal of Transnational Studies 1 (1): 83–99. Sassaman, Kenneth E. 2011. Early Pottery in the Southeast: Tradition and Innovation in Cooking Technology. The University of Alabama Press. Sathasivam, Sathish, R.S. Kankara, S. Chenthamil Selvan, Manikandan Muthusamy, Arockiaraj Samykannu, and Rajan Bhoopathi. 2015. “Textural Characterization of Coastal Sediments along Tamil Nadu Coast, East Coast of India.” Procedia Engineering 116:794–801. Schiffer, Michael B. 1990. “The Influence of Surface Treatment on Heating Effectiveness of Ceramic Vessels.” Journal of Archaeological Science 17 (4): 373–81. Scholliers, Peter. 2001. “Meals, Food Narratives, and Sentiments of Belonging in Past and Present”. In Food, Drink and Identity: Cooking, Eating and Drinking in Europe since the Middle Ages. Edited by Peter Scholliers, 3-22. Oxford. Schortman, Edward M, Patricia A Urban, and others. 1998. “Culture Contact Structure and Process.” In Studies in Culture Contact: Interaction, Culture Change, and Archaeology. Edited by James G. Cusick, 102–25. Southern Illinois University. Schubert, P. 1986. “Petrographic Modal Analysis-a Necessary Complement to Chemical Analysis of Ceramic Coarse Ware.” Archaeometry 28 (2): 163–78. Scollar, Irwin. 1978. “Computer Image Processing for Archaeological Air Photographs.” World Archaeology 10 (1): 71–87. Shelach, Gideon. 2016. Prehistoric Societies on the Northern Frontiers of China: Archaeological Perspectives on Identity Formation and Economic Change during the First Millennium BCE. Routledge. Shepard, Anna Osler. 1936. The Technology of Pecos Pottery. Yale University Press. Silliman, Stephen W. 2013. “What, Where, and When Is Hybridity.” In The Archaeology of Hybrid Material Culture. Edited by Jeb J. Card, 486-500. SIU Press. 238 Smith, A Livingstone. 2001. “Bonfire II: The Return of Pottery Firing Temperatures.” Journal of Archaeological Science 28 (9): 991–1003. Stark, Miriam T. 1998. “Technical Choices and Social Boundaries in Material Culture Patterning: An Introduction”. In The Archaeology of Social Boundaries. Edited by Miriam Stark, 1-11. Smithsonian Institution Press. Stark, Miriam T, Ronald L Bishop, and Elizabeth Miksa. 2000. “Ceramic Technology and Social Boundaries: Cultural Practices in Kalinga Clay Selection and Use.” Journal of Archaeological Method and Theory 7:295–331. Stein, Gil. 1999. Rethinking World-Systems: Diasporas, Colonies, and Interaction in Uruk Mesopotamia. University of Arizona Press. Stein, Gil J. 2012. “Food Preparation, Social Context, and Ethnicity in a Prehistoric Mesopotamian Colony.” In The Menial Art of Cooking: Archaeological Studies of Cooking and Food Preparation. Edited by Sarah R. Graff and Enrique Rodríguez-Alegría, 47–63. University Press of Colorado. Stoltman, James B. 1991. “Ceramic Petrography as a Technique for Documenting Cultural Interaction: An Example from the Upper Mississippi Valley.” American Antiquity 56 (1): 103–20. ———. 2001. “The Role of Petrography in the Study of Archaeological Ceramics.” Earth Sciences and Archaeology, 297–326. Stoltman, James B, Zhichun Jing, Jigen Tang, and George Rapp. 2009. “Ceramic Production in Shang Societies of Anyang.” Asian Perspectives, 182–203. Stoltman, James B, Joyce Marcus, Kent V Flannery, James H Burton, and Robert G Moyle. 2005. “Petrographic Evidence Shows That Pottery Exchange between the Olmec and Their Neighbors Was Two-Way.” Proceedings of the National Academy of Sciences 102 (32): 11213–18. Stoltman, James B, Zhanwei Yue, Zhichun Jing, Jigen Tang, James H Burton, and Mati Raudsepp. 2018. “New Insights into the Composition and Microstructure of Ceramic Artifacts Associated with the Production of Chinese Bronzes at Yinxu, the Last Capital of the Shang Dynasty.” Archaeological Research in Asia 15:88–100. Stone, Jane. 1991. “Computer Applications in Archaeology: Graphics, Database, and Image Processing in a Multimedia Field-to-Publication Data Management System.” Social Science Computer Review 9 (1): 39–61. 239 Sun, Li-qun, Xing Liang, Bin Ma, Jing Li, Xin Zhang, Chen Song, and Lin Li. 2021. “Characteristics of Sedimentary Environment since Quaternary in Northern Jianghan Basin, China: Reconstruction of Paleoenvironments.” Quaternary International 589:12–24. Sun Zhuo. 2017. “A Preliminary Study on the Evolution of Pottery from Panlongcheng 盘龙城遗址出土陶器演变初探.” Jianghan Archaeology 江汉考古 3: 57–66. Sun, Zhuo, Su Xin, Xiaohong Wu, Yan Pan, Hui Chen, Qiushi Zou and Jindong Lu. 2020. “Recent Radiocarbon Dates for Panlongcheng (近年来盘龙城遗址的碳十四年代测定).” Jianghan Archaeology 江汉考古 6:103-115. Thér, R. 2014. “Identification of Pottery Firing Structures Using the Thermal Characteristics of Firing.” Archaeometry 56:78–99. Torleàrrd, AKI. 1986. “Some Photogrammetric Experiments with Digital Image Processing.” The Photogrammetric Record 12 (68): 175–96. Trindade, Maria J., M.I. Dias, João Coroado, and Fernando Rocha. 2009. “Mineralogical Transformations of Calcareous Rich Clays with Firing: A Comparative Study between Calcite and Dolomite Rich Clays from Algarve, Portugal.” Applied Clay Science 42 (3–4): 345–55. Tucker, Maurice E. 2001. Sedimentary Petrology: An Introduction to the Origin of Sedimentary Rocks. John Wiley & Sons. Twiss, Kathryn. 2007. “Introduction”. In The Archaeology of Food and Identity. Edited by Kathryn Twiss. Center for Archaeological Investigations: Southern Illinois University Carbondale. ———. 2012. “The Archaeology of Food and Social Diversity.” Journal of Archaeological Research 20:357–95. Vandenberghe, N. 1975. “An Evaluation of CM Patterns for Grain-Size Studies of Fine Grained Sediments.” Sedimentology 22 (4): 615–22. Velraj, G, K Janaki, A Mohamed Musthafa, and R Palanivel. 2009. “Estimation of Firing Temperature of Some Archaeological Pottery Shreds Excavated Recently in Tamilnadu, India.” Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy 72 (4): 730–33. Vermeulen, Hans, and Cora Govers. 1994. “The Anthropology of Ethnicity: Beyond Ethnic Groups and Boundaries”. Current Anthropology 37(4): 722-723. 240 Visher, Glenn S. 1969. “Grain Size Distributions and Depositional Processes.” Journal of Sedimentary Research 39 (3). Vletter, Willem F. 2014. “(Semi) Automatic Extraction from Airborne Laser Scan Data of Roads and Paths in Forested Areas.” In Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014), 9229:400–410. SPIE. Voss, Barbara L. 2015. “What’s New? Rethinking Ethnogenesis in the Archaeology of Colonialism.” American Antiquity 80 (4): 655–70. Waber, Nicholas Hans. 2020. “Open Lithics: Applying Open Source Technologies to Problems in Lithic Use Wear Experimentation and Analysis.” PhD Thesis, University of British Columbia. Wade, Nicholas J, Dejan Todorović, David Phillips, and Bernd Lingelbach. 2017. “Johann Joseph Oppel (1855) on Geometrical–Optical Illusions: A Translation and Commentary.” I-Perception 8 (3). Wallisch, Pascal. 2017. “Illumination Assumptions Account for Individual Differences in the Perceptual Interpretation of a Profoundly Ambiguous Stimulus in the Color Domain: ‘The Dress.’” Journal of Vision 17 (4): 5–5. Wang, Bian, Lin Fang, Qin Fang. 2016. Treasure in the South: The Material Culture of Panlongcheng in the Shang Period (Exhibition Catalogue). Wuhan: Wuhan Museum. Wang, Haicheng. 2014. “China’s First Empire? Interpreting the Material Record of the Erligang Expansion.” Art and Archaeology of the Erligang Civilization. Edited by Kyle Steinke, 67–97. Princeton University Press. Wang, Qingzhu, Siran Liu, Jianli Chen, Yanxiang Li, Jianfeng Lang, Xiaodong Guo, Xuexiang Chen, and Hui Fang. 2023. “The First Discovery of Shang Period Smelting Slags with Highly Radiogenic Lead in Yingcheng and Implications for the Shang Political Economy.” Journal of Archaeological Science 149:1-9. Wang, Xiaocui, Cheng Zhu, Li Wu, W Sun, F Li, F Zhou, S Qi, and Q Gong. 2012. “Grain-Size Characteristics and Sedimentary Environment Change of JZ-2010 Section in Jianghan Plain, Hubei Province.” Journal of Lake Sciences 24 (3): 480–86. Wilcock, John, and T. Coombes. 1986. “Some Further Developments in Hardware and Software for the Automatic Capture of Artefact Shapes by Television Camera.” In Computer Applications in Archaeology 1985. Proceedings of the Conference on Quantitative Methods, Institute of Archaeology, London, March 29-30, 1985. Edited by E. Webb, 145-151. Institute of Archaeology, University of London. 241 Wilkie, Laurie A, and Paul Farnsworth. 2005. “Sampling Many Pots: An Archaeology of Memory and Tradition at a Bahamian Plantation.” African Diaspora Archaeology Newsletter 8 (4): 4. Wong, H.S., M.K. Head, and N.R. Buenfeld. 2006. “Pore Segmentation of Cement-Based Materials from Backscattered Electron Images.” Cement and Concrete Research 36 (6): 1083–90. Wynn, Jeffrey C. 1986. “A Review of Geophysical Methods Used in Archaeology.” Geoarchaeology 1 (3): 245–57. Xu, Shen. 2018. “On the existence of imitation imprinted hard pottery in Panlongcheng 试论盘龙城存在的模仿印纹硬陶.” Jianghan Archaeology 江汉考古 5: 126-131. Ying, Qin, Guofeng Wei, and Qiao Huang. 2020. “The Provenance of Yin-Shang Bronzes with Highly Radiogenic Lead Isotopes.” Archaeological and Anthropological Sciences 12:1–10. Zhang Changping. 2020. “Regarding the nature of Panlongcheng 关于盘龙城的性质.” Jianghan Archaeology 江汉考古 6:53–56. Zhang, Changping, Kyle Steinke, and D.C. Ching. 2014. “Erligang: A Perspective from Panlongcheng.” In Art and Archaeology of the Erligang Civilization. Edited by Kyle Steinke, 51–63. Princeton University Press. Zhang, Qiang, Cheng Zhu, Tong Jiang, and Stefan Becker. 2005. “Mid-Pleistocene Environmental Reconstruction Based on Xiashu Loess Deposits in the Yangtze Delta, China.” Quaternary International 135 (1): 131–37. Zhang, Hai, Hui Wang, Qiushui Zou, Hui Chen, Xin Su, Hang Liao. 2018. “A preliminary study on the landform evolution of the Panlongcheng settlement during the Shang Dynasty 商代盘龙城聚落地貌演变的初步研究.” Jianghan Archaeology 江汉考古. 5: 139–52. Zhao, Qiguo, and Hao Yang. 1995. “A Preliminary Study on Red Earth and Changes of Quaternary Environment in South China.” Quaternary Sciences 15 (2): 107–16. Zhu, JJ. 1988. “Genesis and Research Significance of the Plinthitic Horizon.” Geographical Research 7 (4): 12–20. Zhu, Letian, Xiaoling Gao, Bin Zhang, Yi Bao, and Jianming Zheng. 2023. “PIXE and FCA Study on the Provenance of Proto-Porcelain of Xia and Shang Dynasty Unearthed 242 from Erlitou and Yinxu Sites, China.” Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 540:110–21. Zou, Qiushi. 2022. “A Study of Landscape Settlement in the Jianghan Plain and its Surrounding Areas during the Xia and Shang Dynasties 江汉平原及周边地区夏商时期聚落景观研究.” Kaogu 考古 1:84-97. Zou, Qiushi and Changping Zhang. 2020. “Summary of Findings at Excavated Localities at the Site of Panlongcheng 武汉市盘龙城遗址各地点历年考古工作综述.” Jianghan Archaeology江汉考古 6:3-18. 243 Appendices Appendix A. Supplemental Material for Chapter 5 A.1 SEM-BSE Images of Untempered Samples – Zhengzhou UBC06-04 244 UBC06-05 245 UBC06-10 246 UBC06-11 247 A.2 SEM-BSE Images of Untempered Samples – Panlongcheng UBC06-20 248 UBC06-38 249 UBC06-39 250 251 UBC06-41 252 UBC06-42 253 UBC06-51 254 A.3 SEM-BSE Images of Gang Samples – Zhengzhou UBC06-06 255 UBC06-08 256 257 UBC06-12 258 A.4 SEM-BSE Images of Gang Samples – Panlongcheng UBC06-21 259 260 UBC06-22 261 UBC06-24 262 UBC06-26 263 UBC06-33 264 A.5 SEM-BSE Images of Li Samples – Zhengzhou UBC06-01 265 UBC06-02 266 UBC06-03 267 A.6 SEM-BSE Images of Li Samples - Panlongcheng UBC06-17 268 UBC06-28 269 UBC06-29 270 UBC06-34 271 UBC06-43 272 273 Appendix B. Supplemental Material for Chapter 6 B.1 SEM-BSE Images of Untempered Samples – Zhengzhou UBC06-05 274 UBC06-10 275 UBC06-11 276 B.2 SEM-BSE Images of Untempered Samples – Panlongcheng UBC06-39 277 UBC06-41 278 B.3 SEM-BSE Images of Gang Samples – Zhengzhou UBC06-12 279 UBC06-13 280 B.4 SEM-BSE Images of Gang Samples – Panlongcheng UBC06-21 281 UBC06-22 282 UBC06-33 283 B.5 SEM-BSE Images of Li Samples – Zhengzhou UBC06-01 284 UBC06-02 285 UBC06-03 286 B.6 SEM-BSE Images of Li Samples – Panlongcheng UBC06-28 287 UBC06-29 288 UBC06-34 289 B.7 SEM-BSE Images of Zun/Protoporcelain - Panlongcheng UBC06-32 (Protoporcelain) 290 291 UBC06-42 (Zun) 292 Appendix C. Supplemental Material for Chapter 7 C.1 SEM-BSE Images of Molds – Yinxu 09AG11 293 09AG13 09AG15 294 09AG16 295 C.2 SEM-BSE Images of Molds - Panlongcheng UBC06-101 296 UBC06-102
Thesis/Dissertation
eng
Anthropology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Jing, Zhichun
Cultural transformation and diaspora during the Erligang Period : a case study via digital image analysis of Panlongcheng ceramic microstructure
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