A Neolithic Lapidary Workshop at the Lower Reaches of Yangtze River: Chaîne Opératoire of Quartzite Ornament Production at Fangjiazhou Site.

  • Published In: Asian Perspectives: Journal of Archeology for Asia & the Pacific, 2025, v. 64, n. 1. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Wen, Yadi; FANG Xiangming; SHI Yong 3 of 3

Abstract

The production and consumption of ornaments played an important role in the lower reaches of Yangtze River during the Chinese Neolithic. This study analyzes the chaîne opératoire of quartzite ornament production at Fangjiazhou, the earliest and largest known Neolithic workshop site in the lower Yangtze River area. Dated 6000-5600 b.p., from Late Majiabang Culture to Early Songze Culture, the site demonstrates remarkable transitions in lapidary production, from raw material selection to technological organization, during the Songze Culture period, together with significant sociocultural changes including an increased number of settlements, emergent social hierarchy, regional differences, and extensive cultural exchanges with surrounding areas. Evidence for spatial organization, craft specialization, and technological choices associated with raw material selection is examined within the distinctive transitional sociocultural context of the Songze Culture to better understand the organization of production as a whole. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Asian Perspectives: Journal of Archeology for Asia & the Pacific. 2025/01, Vol. 64, Issue 1, p1
  • Document Type:Article
  • Subject Area:Power and Energy
  • Publication Date:2025
  • ISSN:0066-8435
  • DOI:10.1353/asi.2025.a968064
  • Accession Number:187273645
  • Copyright Statement:Copyright of Asian Perspectives: Journal of Archeology for Asia & the Pacific is the property of University of Hawai'i and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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