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.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".