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Record W2329924990 · doi:10.1177/000841740907600113

Hunter-Gatherers and the Behavioural Ecology of Human Occupation

2009· article· en· W2329924990 on OpenAlexvenueaboutno aff
Mark Hudson, Mami Aoyama

Bibliographic record

VenueCanadian Journal of Occupational Therapy · 2009
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Therapy Practice and Research
Canadian institutionsnot available
FundersJohns Hopkins University
KeywordsIndigenousForagingProvisioningDiversification (marketing strategy)EcologyGeographyDemographySociologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Despite growing interest in indigenous peoples within occupational therapy in Canada and elsewhere, there has been little consideration of hunter-gathering-an occupation that retains great material and symbolic significance for many indigenous groups. PURPOSE: A preliminary analysis of occupational behaviour amongst hunter-gatherers was conducted to aid understanding of the nature and evolution of human occupations and inform policy in indigenous occupational therapy. METHODS: Human behavioural ecology was used to analyze four aspects of hunter-gatherer occupations: occupational diversification, the sexual "division of labour," the long dependence of juveniles on adult provisioning, and active foraging by postmenopausal women. FINDINGS: It was concluded that many occupational adaptations of human foragers can be related to life-history traits, namely slow maturation, long lifespans, weaning before independent feeding, and postmenopausal longevity. IMPLICATIONS: Further research will help understand how our hunter-gatherer heritage has affected the evolution of occupational behaviour and to develop program designs using foraging occupations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.293
GPT teacher head0.515
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations5
Published2009
Admission routes2
Has abstractyes

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