Why group apologies succeed and fail: Intergroup forgiveness and the role of primary and secondary emotions.
Bibliographic record
Abstract
It is widely assumed that official apologies for historical transgressions can lay the groundwork for intergroup forgiveness, but evidence for a causal relationship between intergroup apologies and forgiveness is limited. Drawing on the infrahumanization literature, we argue that a possible reason for the muted effectiveness of apologies is that people diminish the extent to which they see outgroup members as able to experience complex, uniquely human emotions (e.g., remorse). In Study 1, Canadians forgave Afghanis for a friendly-fire incident to the extent that they perceived Afghanis as capable of experiencing uniquely human emotions (i.e., secondary emotions such as anguish) but not nonuniquely human emotions (i.e., primary emotions such as fear). Intergroup forgiveness was reduced when transgressor groups expressed secondary emotions rather than primary emotions in their apology (Studies 2a and 2b), an effect that was mediated by trust in the genuineness of the apology (Study 2b). Indeed, an apology expressing secondary emotions aroused no more forgiveness than a no-apology control (Study 3) and less forgiveness than an apology with no emotion (Study 4). Consistent with an infrahumanization perspective, effects of primary versus secondary emotional expression did not emerge when the apology was offered for an ingroup transgression (Study 3) or when an outgroup apology was delivered through an ingroup proxy (Study 4). Also consistent with predictions, these effects were demonstrated only by those who tended to deny uniquely human qualities to the outgroup (Study 5). Implications for intergroup apologies and movement toward reconciliation are discussed.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| 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".