Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 |
| 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 it