Ethnography and ethnohistory support the efficiency of hunting through endurance running in humans
Bibliographic record
Abstract
Humans have two features rare in mammals: our locomotor muscles are dominated by fatigue-resistant fibres and we effectively dissipate through sweating the metabolic heat generated through prolonged, elevated activity. A promising evolutionary explanation of these features is the endurance pursuit (EP) hypothesis, which argues that both traits evolved to facilitate running down game by persistence. However, this hypothesis has faced two challenges: running is energetically costly and accounts of EPs among late twentieth century foragers are rare. While both observations appear to suggest that EPs would be ineffective, we use foraging theory to demonstrate that EPs can be quite efficient. We likewise analyse an ethnohistoric and ethnographic database of nearly 400 EP cases representing 272 globally distributed locations. We provide estimates for return rates of EPs and argue that these are comparable to other pre-modern hunting methods in specified contexts. EP hunting as a method of food procurement would have probably been available and attractive to Plio/Pleistocene hominins. Using foraging theory and ethnohistoric data, the authors’ analysis supports the hypothesis that the human ability to sweat while running long distances evolved in the context of persistent, endurance-based pursuits of game.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".