The Effects of Safety-Related Abusive Supervision and Gender on Attributions and Safety Outcomes
Bibliographic record
Abstract
While most research implies the harmful effects of abusive supervision, we argue that there are specific contextual factors where abusive supervision is more likely to be attributed to performance promotion intentions as opposed to injury initiation ones. We present the concept of Safety-Specific Abusive Supervision, which we define as perceptions of abusive supervisory behaviors related to safety that occur in hazardous work environments. We theorize that leaders who engage in Safety-Specific Abusive Supervision are more likely to have their behaviors attributed to performance promotion motives such that their actions are perceived to keep employees safe and motivate safety performance. We suspect that those performance promotion attributions act as a mediator between safety-specific abusive supervision and safety performance outcomes, namely safety voice and safety climate. However, we predict that this relationship is only true for male supervisors and not female supervisors. Across three studies (two experimental studies and one field study), we largely find support for our hypotheses. We conclude with a discussion of the importance of examining contextual factors for examining the attributions of abusive supervision and implications for managers of work safety relevant environments.
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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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".