Bibliographic record
Abstract
Purpose The purpose of this paper is to apply insights from social role theory to trust repair, highlighting the underexplored implications of gender. Trust repair may be more difficult following violations that are incongruent with the transgressor’s gender role. Design/methodology/approach This paper reviews research on trust repair, particularly Kim et al.’s (2004, 2006) discovery that apologizing with internal attributions is best for ability-related violations and denying responsibility is best for integrity-related violations. Propositions about trust repair are grounded in attribution and social role theory. Findings Trust violations may incur a bigger backlash when they are incongruent with gender roles, particularly for individuals in gender-incongruent professions and cultures with low gender egalitarianism. Men may find ability-related violations more difficult to repair. Women may find repairing benevolence and integrity-related violations more difficult. When apologies are offered, attributions that are consistent with gender roles (internal attributions for men, external attributions for women) may be most effective. Practical implications Gender can be a relevant factor in trust repair. Policies and training addressing conflict should consider how these differences manifest. Originality/value Gender role differences have largely been overlooked in trust repair. By integrating social role theory and exploring benevolence-based violations, this paper offers a more complete understanding of trust repair.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.027 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".