Walk the Talk: The Effects of Apology and Reparation After Acts of Prejudice
Bibliographic record
Abstract
ABSTRACT In an era of abundant high‐profile apologies, many of which are perceived to be cheap and insincere, it is crucial to understand what constitutes a meaningful response from a high‐status perpetrator. Across three studies using a 2 (apology: present, absent) × 2 (reparation: present, absent) within‐subjects design, we presented participants ( N total = 300) with 16 vignettes describing prejudicial harm and assessed the unique effects of apology and reparation on perceptions of the perpetrators' subsequent responses. We additionally examined whether apology and reparation operate via a cognitive mechanism (reevaluation of the harm itself) or via a relational mechanism (identification with the perpetrator). As predicted, the presence of an apology and of reparation each independently predicted more positive perceptions of the perpetrator's response. Reparation exerted a stronger effect than apology on ratings of response quality (Study 1) and on ratings of the response's impact (Studies 2 and 3); in some cases, if reparation was present, apology did not add value. Our findings suggest that, while apology operates primarily via a relational mechanism, reparation operates via both cognitive and relational mechanisms. Additionally, responses were perceived more favorably overall in the context of close relationships (Study 3), which contributes to existing evidence that relational closeness buffers against negative attributions about the perpetrator and their motives. We suggest that while apology and reparation are each key to an effective response, reparation plays a particularly important role in predicting positive reception to a response to prejudicial harm.
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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 | 0.004 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".