46 Evolutionary Anthropology Social Foraging and the Behavioral Ecology of In tragrou p Resource Transfers
Bibliographic record
Abstract
Two chimpanzees stalk, isolate, and kill a red colobus monkey. An attendant primatologist notes that parts of the prey are relinquished selectively to onlooking scroungers (Fig. 1). A human forager returns to camp mid-afternoon with a freshly killed, medium-sized ungulate. Later in the day, an ethnographer observes that shared portions of the animal have found their way into the cooking pots of most or all of those in the small band. Examining a prehistoric scatter of food residues, an ethnoarcheologist wonders when early hominids began to scrounge or share food, and with what consequences for our evolution. All of these settings represent one problem: the analysis of intragroup resource transfers among social foragers. New studies in the behavioral ecology of transfers show them to be more commonplace in nature, more complicated and variable, and more subject to comparative analysis than has been appreciated. Food sharing has been a routine ob-servation in hunter-gatherer studies. Scrounging is a more recent, but not uncommon observation for some pri-mates. At one time, the explanations for these practices seemed clear. Hunter-gatherers, living in small, sta-ble groups, enact their social alle-giances and secure a more regular diet through the institutionalized sharing of food. Obvious group benefits are served. For chimpanzees, prey trans-fer has been seen as something the Bruce Winterhalder studies evolutionary ecology models as a means of understanding the origins of behavioral diversity among foragers and peasant food producers. He has undertaken ethnographic field work with hunter-gatherers of the boreal forest in Canada and with the Quechua, agriculturists in a tropical high-mountain region of Peru. He has published on diet selection, risk avoidance and sharing, predator-prey population dynamics, and plant domestication and exchange. With Eric Smith, he is co-editor of the volume
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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 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".