Costly teaching contributes to the acquisition of spear hunting skill among BaYaka forager adolescents
Bibliographic record
Abstract
Teaching likely evolved in humans to facilitate the faithful transmission of complex tasks. As the oldest evidenced hunting technology, spear hunting requires acquiring several complex physical and cognitive competencies. In this study, we used observational and interview data collected among BaYaka foragers (Republic of the Congo) to test the predictions that costlier teaching types would be observed at a greater frequency than less costly teaching in the domain of spear hunting and that teachers would calibrate their teaching to pupil skill level. To observe naturalistic teaching during spear hunting, we invited teacher-pupil groupings to spear hunt while wearing GoPro cameras. We analysed 68 h of footage totalling 519 teaching episodes. Most observed teaching events were costly. Direct instruction was the most frequently observed teaching type. Older pupils received less teaching and more opportunities to lead the spear hunt than their younger counterparts. Teachers did not appear to adjust their teaching to pupil experience, potentially because age was a more easily accessible heuristic for pupil skill than experience. Our study shows that costly teaching is frequently used to transmit complex tasks and that instruction may play a privileged role in the transmission of spear hunting knowledge.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".