A Critical Review of Official Public Apologies: Aims, Pitfalls, and a Staircase Model of Effectiveness
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
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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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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