Childhood Learning and the Distribution of Knowledge in Foraging Societies
Bibliographic record
Abstract
A differential distribution of knowledge is characteristic of all human societies, and in relatively egalitarian foraging societies, in which age and gender tend to structure the few distinct social roles available, the distribution of cultural knowledge is also expected to occur along these lines. In this chapter, I consider the relationship between child-rearing practices and the distribution of cultural knowledge across social roles. In particular, I look at gendered patterns of knowledge and decision-making, drawing upon foraging societies in three different environmental zones as case studies for comparison: the eKung San of the Kalahari Desert, the Aka Pygmies of the central African forests, and the Utku and Nunamiut Eskimo of northern Alaska and Canada. Child-rearing practices vary among the three groups considered here and are found to relate to the distribution of knowledge and skills across gender roles. This, in turn, may be among the factors influencing group decision-making patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".