Why do children and adults think other people punish?
Bibliographic record
Abstract
Past research has demonstrated that both consequentialist motives (such as deterrence) and deontological motives (such as ‘just deserts’) underlie children’s and adults’ punitive behavior. But what motives do we ascribe to others who pursue punishment? The present work explores this question by assessing which punitive motives children (6- and 7-year-olds, n = 100; 67% white; 55% female) and adults (n = 100; 76% white; 35% female) attribute to individuals who witnessed and punished a transgression (third-party punishment). Beyond this, we varied the social role of the punisher (a teacher, an adult visiting a school, a fellow peer) to examine whether motivational ascriptions vary depending on the social context. Across these contexts, children endorsed a variety of punishment motives but consistently rejected the notion that individuals punish for the purpose of inflicting suffering. Adults—like children—prioritized consequentialist motives but, in more personal contexts (involving a child punishing their peer), considered ‘just deserts’ a more plausible motive. These findings speak to developmental and contextual variation in individuals’ theories about punitive motives and provide insight into how individuals understand and respond to punishment in everyday life.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".