The impact of group membership on punishment versus partner rejection
Bibliographic record
Abstract
People often display ingroup bias in punishment, punishing outgroup members more harshly than ingroup members. However, the impact of group membership may be less pronounced when people are choosing whether to stop interacting with someone (i.e., partner rejection). In two studies (N = 1667), we investigate the impact of group membership on both response types. Participants were assigned to groups based on a "minimal" groups paradigm (Study 1) or their self-reported political positions (Study 2) and played an incentivized economic game with other players. In this game, participants (Responders) responded to other players (Deciders). In the Punishment condition, participants could decrease the Decider's bonus pay. In the Partner Rejection condition, participants could reject future interactions with the Decider. Participants played once with an ingroup member and once with an outgroup member. To control for the effects of intent and outcome, scenarios also differed based on the Decider's Intent (selfish versus fair) and the Outcome (equal versus unequal distribution of resources). Participants punished outgroup members more than ingroup members, however group membership did not influence decisions to reject partners. These results highlight partner rejection as a boundary condition for the impact of group on responses to transgressions.
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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 | 0.006 | 0.042 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".