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Record W4396535930 · doi:10.1016/j.jasrep.2024.104560

Evidence of an age and/or gender-based division of labor during the Last Glacial Maximum in Iberia through rabbit hunting

2024· article· en· W4396535930 on OpenAlexafffund
Samuel Seuru, Ariane Burke, Liliana Pérez

Bibliographic record

VenueJournal of Archaeological Science Reports · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPleistocene-Era Hominins and Archaeology
Canadian institutionsUniversité du Québec à MontréalUniversité de Montréal
FundersFonds de Recherche du Québec-Société et CultureUniversity of Colorado
KeywordsGlacial periodRabbit (cipher)Last Glacial MaximumDivision (mathematics)Division of labourGeographyGeologyArchaeologyPaleontologyPolitical scienceStatisticsMathematicsLawArithmetic

Abstract

fetched live from OpenAlex

• Age and/or gender-based division of labor during the Last Glacial Maximum in Iberia. • Implementation of an Agent-Based Model grounded in the Optimal Foraging Theory. • Technological, social and economic implications of rabbit hunting. Many archaeological assemblages from the Iberian Peninsula dated to the Last Glacial Maximum contain large quantities of European rabbit (Oryctolagus cuniculus) remains with an anthropic origin. Ethnographic and historic studies report that rabbits may be mass-collected through warren-based harvesting involving the collaborative participation of several persons. We propose and implement an Agent-Based Model grounded in the Optimal Foraging Theory and the Diet Breadth Model to examine how different warren-based hunting strategies influence the resulting human diets. We then visually compare the simulated diets with the zooarchaeological record. Our simulation outputs suggest that rabbits may have been mass collected from warrens by humans through the use of nets and an age and/or gender-based division of labor. Our results have profound implications for the comprehension of hunting behavior and the social organization of humans during the Last Glacial Maximum in the Iberian Peninsula.

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)0.007
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.062
GPT teacher head0.356
Teacher spread0.294 · 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.

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

Citations1
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Archaeological Science ReportsSame topicPleistocene-Era Hominins and ArchaeologyFrench-language works237,207