Why group apologies succeed and fail: Intergroup forgiveness and the role of primary and secondary emotions.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it