Woman the Hunter? Female foragers sometimes hunt, yet gendered divisions of labor are real
Bibliographic record
Abstract
Abstract Gendered divisions of labor are a feature of every known contemporary hunter-gatherer (forager) society. While gender roles are certainly flexible, and prominent and well-studied cases of female hunting do exist, it is more often men who hunt. A new study (Anderson et al., 2023) surveyed ethnographically known foragers and found that women hunt in 79% of foraging societies, with big-game hunting occurring in 33%. Based on this single type of labor, which is one among dozens performed in foraging societies, the authors question the existence of gendered division of labor altogether. As a diverse group of hunter-gatherer experts, we find that claims that foraging societies lack or have weak gendered divisions of labor are contradicted by empirical evidence. We conducted an in-depth examination of Anderson et al. (2023) data and methods, finding evidence of sample selection bias and numerous coding errors undermining the paper’s conclusions. Anderson et al. (2023) have started a useful dialogue to ameliorate the popular misconception that women never hunt. However, their analysis does not contradict the wide body of empirical evidence for gendered divisions of labor in foraging societies. Furthermore, a myopic focus on hunting diminishes the value of contributions that take different forms and downplays the trade-offs foragers of both sexes routinely face. We caution against ethnographic revisionism that projects Westernized conceptions of labor and its value onto foraging societies.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".