Harming In Order To Help: An Empirical Characterization of Prosocial Aggression
Bibliographic record
Abstract
People sometimes inflict harm with the intent to help the very target of their aggression. Across six studies (N = 1,527), we examined the nature of such prosocial aggression. Many participants believed that altruistically-motivated aggression exists and most believed their aggression was more altruistic than others’ — beliefs that were positively associated with antisocial and prosocial traits. Translating beliefs to behavior, participants were often prosocially-aggressive — inflicting more harm when their aggression could also help (versus only hurt) the target. Prosocial aggression was elevated towards agreeable (versus antagonistic) people, robust to whether it was personally costly or not, and sensitive to both the degree of harm it inflicted and help it conferred. It was unassociated with antisocial and prosocial traits, failing to map neatly onto agreeable or antagonistic tendencies. Our findings characterize a novel aggression phenotype and highlight the need to understand how people often intentionally harm those they wish to help.
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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 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".