Who’s to blame? Dissimilarity as a cue in moral judgments of observed ostracism episodes.
Bibliographic record
Abstract
When observing an ostracism episode, observers may wish to know whether ostracism is justified or not. If ostracism appears unjustified, observers will likely blame the sources and sympathize with the target; if it appears justified, observers will likely blame and devalue the target. Here we introduce the "social dissimilarity rule," which holds that observers base their moral judgments on dissimilarities between the members of the observed group. In five studies, participants either recalled observed ostracism episodes or observed group interactions in which one group member was ostracized (e.g., in a chat or a group-working task). Results show that if similar persons exclude a dissimilar target (target is an "odd-one-out"), observers attribute ostracism to malicious motives of the ostracizers, such as ingroup favoritism, and devalue the ostracizers. However, if ostracism cannot be explained by social dissimilarity between the sources and the target, observers assume that the target is being punished for a norm deviation (punitive motive) and devalue the target. Use of the social dissimilarity rule was neither moderated by cognitive load (Study 3) nor by the perceived essentiality of the group distinction (Study 4). But if participants knew that the target previously deviated from a norm, knowledge about the situation had a stronger effect on moral judgments (Study 5) than social dissimilarity. These findings further our understanding of how observers make moral judgments about ostracism, which is important given that an observer's moral judgment can strongly impact bystander behavior and thus target recovery and well-being. (PsycINFO Database Record
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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 | 0.033 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".