Bibliographic record
Abstract
Introduction Human hunting strategies, like those of many non-human primates, vary seasonally with fluctuations in prey abundance, encounter rates, and profitability (Winterhalder 1981; Smith 1991). Temporality in resource supply has profound social effects as well, and some of the earliest studies of hunter–gatherers emphasized the impact of seasonality on settlement size, mobility, general economic organization, and even property rights, religion, family structure, and the sexual division of labor (Mauss & Beuchat 1906; Thomson 1936). For Mauss and Beuchat (1906), seasonality meant temperature: they suggested that Inuit families were organized very differently in the summer than in the winter as a result of the nature of changes in foraging opportunities. For Thomson (1936), seasonality meant rainfall, commenting that the effect of distinct wet and dry seasons in northern Australia might lead one to think that they were observing two different “tribes” of people. Anthropological interest in seasonality and its effects on human social organization has waned since then, frustrated by an inability to find correlations between seasonality and human behavior. Our goal in this chapter is to explore the utility of two approaches to understanding the relationship between seasonality and social behavior. One attempts to use comparative ecological data across groups to explain differences in aspects of social and economic behavior such as mobility and land tenure decisions; the other examines how different individuals within a group may respond differently to resource seasonality.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".