Remembering the People in Peopling Narratives: Landscape Learning as a Bridge between Traditional Knowledge and Archaeology
Author(s): Nicholas Schmuck
Year: 2023
Summary
This is an abstract from the "Archaeology and Landscape Learning for a Climate-Changing World" session, at the 88th annual meeting of the Society for American Archaeology.
The debate over the Peopling of the Americas is one of grand narratives and contested archaeological evidence. The Landscape Learning Framework provides a mechanism for approaching the archaeological record at a difference scale, allowing us to rehumanize the study of population expansions in the terminal Pleistocene. Informed by a growing general understanding of the ways that humans learn unfamiliar environments, we can begin to predict how people might choose to first settle in a new land, and examine tensions between old and new “ways of doing things” in new landscapes. Disentangled from the host of implications that accompany continental-scale migration paradigms, we can more easily integrate regional archaeological analyses with Traditional Knowledge and the oral traditions of populations who have occupied these landscapes from time immemorial. In coastal southeast Alaska, pairing meaningful oral traditions with testable hypotheses generated through the landscape learning framework provides clear routes for exploring the human experience of dynamic post-glacial sea-level change, despite the current shortcomings of the archaeological record.
Cite this Record
Remembering the People in Peopling Narratives: Landscape Learning as a Bridge between Traditional Knowledge and Archaeology. Nicholas Schmuck. Presented at The 88th Annual Meeting of the Society for American Archaeology. 2023 ( tDAR id: 473749)
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Keywords
General
Coastal and Island Archaeology
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Geoarchaeology
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Landscape Learning
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Paleoindian and Paleoamerican
Geographic Keywords
North America: Arctic and Subarctic
Spatial Coverage
min long: -169.453; min lat: 50.513 ; max long: -49.043; max lat: 72.712 ;
Record Identifiers
Abstract Id(s): 37522.0