Theorizing Prehistoric Large Low-Density Settlements beyond Urbanism and Other Conventional Classificatory Conventions

Part of: Society for American Archaeology 89th Annual Meeting, New Orleans, LA (2024)

This collection contains the abstracts of the papers presented in the session entitled "Theorizing Prehistoric Large Low-Density Settlements beyond Urbanism and Other Conventional Classificatory Conventions" at the 89th annual meeting of the Society for American Archaeology.

Archaeologists are increasingly unveiling evidence that defies conventional classificatory models whereby the development of sociopolitical complexity was a unilinear, stepwise, and standardized process. The prehistoric large low-density settlements are among such evidence and are enabling scholars to acknowledge the organizational plurality and diversity intrinsic to the human past. After decades of being neglected analytically and comparatively, at best considered anomalous cases of ancient urbanism, these settlements are becoming a prolific field for archaeological discussions about the challenges of large population aggregation. Recently documented cases strongly suggest that they can appear without following the incremental increase in complexity that previous anthropological models predicted. In the absence of this prescriptive prelude, archaeologists have started recognizing various developmental trajectories not even considered possible in the past. This session aims to contribute to these discussions by analyzing and comparing worldwide low-density settlements, allowing us to theorize the social, cultural, economic, and political processes underpinning them. By foregrounding the issues above, we avoid imposing long-lasting classificatory conventions that tend to assume monolithic governance apparatuses, integration mechanisms, ideological systems, and subsistence regimes. The session adds to the scholarship on low-density settlement patterns by providing new datasets and avoids the yardstick problem by critically analyzing these data.