A Critical Review of Official Public Apologies: Aims, Pitfalls, and a Staircase Model of Effectiveness
Bibliographic record
Abstract
The world has entered into an “age of apology,” in which governments, armies, and corporations have increasingly begun apologizing for their role in committing historical and contemporary harms. Although it is widely assumed that such apologies help promote intergroup forgiveness, this assumption has not been subjected to a great deal of empirical investigation, and the little research that exists presents a mixed picture. In this article, we present some of the political and ideological arguments for and against providing intergroup apologies. We then critically review the research on the outcomes of apologies, with an eye to developing concrete strategies for maximizing apology effectiveness. Drawing on these discussions, a staircase model for effective intergroup apologies is offered that has implications for social policy. Although we present some pessimism regarding the outcome of intergroup apologies, this article provides arguments for the necessity of formal intergroup apologies and for policy that maximizes their positive effects.
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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 | 0.046 | 0.159 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".