Bibliographic record
Abstract
Food sharing is arguably the foundation from which feasting emerged, and it should be considered as one form of food sharing behavior. Food sharing among nonkin is viewed as one of the hallmarks of human evolution and a core feature of human societies (Gurven 2004; Stevens and Gilby 2004; Bullinger et al. 2013:51). It is considered by some to be the prime mover in social evolution from proto-hominids to modern humans in terms of its possible role in developing cooperation, sociality, sexual division of labor, morality, altruism, and perhaps human economic systems (Isaac 1978; Kurland and Beckerman 1985; Gurven et al. 2000a,b; Dubuc et al. 2012:73). Yet the roots of food sharing appear to go deeper and to have emerged among our nonhuman primate ancestors. Food sharing with kin is relatively common in the animal kingdom (especially mother–offspring sharing), yet food sharing with nonkin among most nonhuman species is absent or very rare, although it does occur to some degree among some nonhuman primate relatives, particularly our closest ancestors, chimpanzees. Food sharing among nonkin does not appear to occur otherwise in the animal kingdom. Understanding why sharing food with nonkin occurs has become a major and controversial area of research. Like feasting, food sharing involves a fundamental paradox: why individuals give away valuable fitness-enhancing food resources (Gurven et al. 2000a:173; 2000b:264). In this chapter, I review some of the contending models regarding the underlying motivations and benefits of food sharing behavior, and I explore the implications of this for the evolution of feasting as one form of food sharing behavior.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".