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Record W3039097074 · doi:10.1017/aaq.2020.34

A Multiscalar Consideration of the Athabascan Migration

2020· article· en· W3039097074 on OpenAlexaboutno aff
Briana Doering, Julie Esdale, Joshua D. Reuther, Senna D. Catenacci

Bibliographic record

VenueAmerican Antiquity · 2020
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsTerritorialityGeographyVolcanoHunter-gathererResource (disambiguation)Adaptation (eye)Subarctic climateEcologyPsychological resilienceArchaeologyEconomic geographyGeologyPaleontologyBiology

Abstract

fetched live from OpenAlex

Genetic and linguistic evidence suggests that, after living in the Subarctic for thousands of years, Northern Athabascans began migrating to the American Southwest around 1,000 years ago. Anthropologists have proposed that this partial out-migration and several associated in situ behavioral changes were the result of a massive volcanic eruption that decimated regional caribou herds. However, regional populations appear to increase around the time of these changes, a demographic shift that may have led to increased territoriality, resource stress, and specialization. Building on existing syntheses of cultural dynamics in the region, analyses of excavated materials, and landscape data from Alaska and Yukon, this research shows that the Athabascan transition represented a gradual shift toward resource specialization in both salmon and caribou with an overall increase in diet breadth, indicating a behavioral transition that is more consistent with gradual demographic change. Further, this behavioral shift was already in motion at the time of the volcanic eruption circa 1150 cal BP and suggests that the ultimate migration from the area was the result of demographic pressures. In sum, this research elaborates on the complex dynamics of resilience and adaptation in hunter-gatherer groups and provides a testable model for explaining past migrations.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.368
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2020
Admission routes1
Has abstractyes

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