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Record W9656916

Causes of toolkit variation among hunter-gatherers: a test of four competing hypotheses

2005· article· en· W9656916 on OpenAlexvenueno aff
Mark Collard, Michael Kemery, Samantha A. Banks

Bibliographic record

VenueCanadian Journal of Archaeology · 2005
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Subsistence agricultureResource (disambiguation)Variation (astronomy)PopulationAffect (linguistics)GeographyEcologyBiologySociologyComputer scienceDemographyAgricultureAnthropology
DOInot available

Abstract

fetched live from OpenAlex

Variation in subsistence-related material culture is an important aspect of the archaeological and ethnographic records, but the factors that are responsible for it remain unclear. Here, we examine this issue by evaluating four factors that may affect the diversity and complexity of the food-getting tools employed by hunter-gatherer populations: 1) the nature of the food resources; 2) risk of resource failure; 3) residential mobility; and 4) population size. We apply step-wise multiple regression analysis to technological and ecological data for 20 hunter-gatherer populations from several regions of the world. The analyses support the hypothesis that risk of resource failure has a significant impact on toolkit diversity and complexity. The results do not support the hypothesis that the characteristics of the resources exploited for food influence toolkit structure, or that residential mobility affects toolkit diversity and complexity. They are also not in line with the hypothesis that population size has an impact on toolkit structure. While our analyses appear to strongly support the suggestion that resource failure risk is the primary influence on hunter-gatherer toolkit structure, we argue that it would be premature to discount the other factors at this stage, and outline the steps that we believe need to be taken next.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.821
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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

Citations125
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ArchaeologySame topicPleistocene-Era Hominins and ArchaeologyFrench-language works237,207