Across six societies children engage in costly third-party punishment of unfair sharing
Bibliographic record
Abstract
Third-party punishment of unfairness shows striking cross-societal variation in adults, yet we know little about where and when in development this variation starts to emerge. When do children across societies begin to pay a cost to prevent unfair sharing? We present an experimental study of third-party punishment of unfair sharing across N = 535 children aged 5-15 from communities in six diverse countries: Canada, India, Peru, Uganda, USA, and Vanuatu. We tested whether children were more likely to punish equal or selfish (maximally unequal) distributions between two absent peers. We also tested whether decisions depended on whether such punishment was costly-participants had to sacrifice their own rewards to punish-or free. Our study generated three main findings. First, children across societies engaged in third-party punishment of selfishness: they were more likely to punish selfish than equal distributions. Second, older children were more likely than younger children to punish selfish sharing in Canada, India, Peru, and the USA. Third, children in Canada and the USA punished more in general in the Free condition than in the Costly condition, whereas children in Uganda punished selfishness more in the Costly condition. These findings show that children from six diverse societal contexts consistently took a stance against unfair sharing, in some cases even sacrificing their own rewards to intervene against selfishness in their peers. We highlight and discuss similarities and differences in cross-societal patterns of age-related differences in third-party punishment and suggest potential explanations for these patterns.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".