Faith in the Just Behavior of the Government: Intergroup Apologies and Apology Elaboration
Bibliographic record
Abstract
After intergroup injustices, perpetrator groups may seek to restore intergroup relations by offering an apology. Through quantitative empirical tests some scholars have examined whether these apologies promote forgiveness and reconciliation. This work has found inconsistent relations between apology and forgiveness. We proposed and tested other variables as relevant outcomes of intergroup apology as well, namely perceived remorsefulness, faith in societal norms of justice, and trust. We also tested how the elaborateness of an apology changed its effectiveness. The study (N = 145) presented excerpts of President Clinton’s apology for the Tuskegee Syphilis Study to African-Americans, varying the apology elaborateness. We examined whether apologies of varying elaborateness affect forgiveness (to be consistent with past research), perceptions that the response was remorseful, beliefs that norms of just behavior would be upheld, and trust in the perpetrator group. All apologies, but particularly more elaborate apologies, resulted in higher perceptions of remorsefulness and justice norms, but not trust or forgiveness. The results imply that apologies may have many benefits with perceptions of remorsefulness and justice norms being amongst them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".