Why Hate the Good Guy? Antisocial Punishment of High Cooperators Is Greater When People Compete To Be Chosen
Bibliographic record
Abstract
When choosing social partners, people prefer good cooperators (all else being equal). Given this preference, people wishing to be chosen can either increase their own cooperation to become more desirable or suppress others' cooperation to make them less desirable. Previous research shows that very cooperative people sometimes get punished ("antisocial punishment") or criticized ("do-gooder derogation") in many cultures. Here, we used a public-goods game with punishment to test whether antisocial punishment is used as a means of competing to be chosen by suppressing others' cooperation. As predicted, there was more antisocial punishment when participants were competing to be chosen for a subsequent cooperative task (a trust game) than without a subsequent task. This difference in antisocial punishment cannot be explained by differences in contributions, moralistic punishment, or confusion. This suggests that antisocial punishment is a social strategy that low cooperators use to avoid looking bad when high cooperators escalate cooperation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".