Assessing the Role of Honour Culture and Image Concerns in Impeding Apologies
Bibliographic record
Abstract
ABSTRACT Despite the known benefits of apologies, people often fail to apologize for wrongdoings. We examined the role of a cultural logic of honour—where apologizing may clash with concerns about maintaining an image of strength and toughness—in reluctance to apologize. Using general population samples from 14 societies in Mediterranean, East Asian and Anglo‐Western regions (N = 5471), we explored links between honour values and norms, image concerns, and apology outcomes using multilevel mediation analyses. Members of groups with stronger honour endorsement reported stronger image concerns about apologizing relative to their concerns about not apologizing, which, in turn, predicted greater reluctance to apologize and fewer past apologies. However, groups with stronger honour endorsement did not show greater reluctance to apologize overall, and some individual‐level facets of honour predicted better apology outcomes. Our results highlight the importance of considering honour as a multifaceted construct and including contextual factors and processes when studying reconciliation processes and obstacles to apologies.
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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 | 0.003 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".