Why Forgiveness is Not Always Forthcoming: Intergroup Apologies, Forgiveness, and the Malleability of Groups
Bibliographic record
Abstract
Intergroup apologies are used widely, but the empirical evidence has produced mixed results as to whether these apologies promote forgiveness, although they have been consistently linked with increased satisfaction. This study draws on recent research regarding groups in conflict to propose beliefs in the malleability of groups (i.e., beliefs in whether groups are able to change or not) moderate responses to an intergroup apology. Two candidates for mediation, namely perceived remorse and re-offense likelihood, were proposed to explain why individuals’ beliefs in whether groups can change or not predict differences in forgiveness and satisfaction following an intergroup apology. To examine this, 204 participants were measured on their beliefs of the malleability of groups. They were then presented with a transgression from a major corporation, followed by an apology or not. The results demonstrated participants who tended to believe groups were capable of change were more forgiving after an apology, compared to when no apology was provided, and this occurred through heightened perceived remorse. This same pattern was evidenced for satisfaction with the response. However, participants who believed groups were unable to change had a tendency to be less forgiving following an intergroup apology compared to when no apology was presented. These participants showed no difference in ratings of satisfaction with the response whether they received an apology or not. This study has significant theoretical implications in clarifying the link between an intergroup apology and forgiveness. In an applied setting, it demonstrates increased forgiveness and satisfaction following an intergroup apology will only occur if recipients believe groups can change.
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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 | 0.010 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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".