The ecological and social context of women’s hunting in small-scale societies
Bibliographic record
Abstract
Women participate in hunting in some foraging societies but not in others. To examine the socioecological factors that are conducive to women’s hunting, we conducted an ethnographic survey using the Human Relations Area Files and other selected sources authored in the past 200 years. Based on life history theory and behavioural ecology, we predicted that women should engage in hunting when: i) it poses few conflicts with childcare, ii) it is associated with few cultural restrictions around the use of hunting technology, iii) it involves low-risk game within range of camp, with the aid of dogs, and/or in groups, and, iv) women fulfil key logistical or informational roles. We systematically reviewed ethnographic documents across 64 societies and coded 242 paragraphs for the above variables. The data largely support theoretical expectations. When women hunted, they did so in a fundamentally different manner than men, focusing on smaller game and hunting in larger groups near camp, often with the aid of dogs. There was little evidence to suggest that women only participated in hunting during non-reproductive years; instead, allocare networks were a prominent strategy for mitigating trade-offs between hunting and childcare responsibilities. Women commonly fulfilled crucial informational, logistical and ritualistic roles. Cultural restrictions limited women’s participation in hunting, but not to the extent commonly assumed. These data offer a cross-cultural framework for making inferences about whether and how women’s hunting occurred in the past.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".