‘I wish I had . . .’: Target reflections on responses to workplace mistreatment
Bibliographic record
Abstract
The aim of this article is to examine target responses to workplace mistreatment and to analyze factors that affect the degree of discrepancy between actual and ‘ideal’ (i.e. desired) responses. Two-hundred and seventeen faculty members at a major research university in North America reported their actual and ideal responses to mistreatment. The most common responses involved passive and social support-seeking strategies. Respondents generally wished they could have been more assertive. The size of the discrepancy between actual and ideal responses to mistreatment was predicted by the perceived severity of the behavior, the coping strategy chosen and a difference in organizational status and gender between the perpetrator and the target of mistreatment. While our findings show that status differences were associated with a larger discrepancy regardless of the direction of the status differences, our results indicate that the mechanisms behind the discrepancy differed. Despite being a relatively high status population, faculty at a prestigious university responded more passively to mistreatment than desired, primarily due to situational constraints. Because the reasons for this discrepancy were often structural (i.e. based on organizational or social status structures), this research highlights the need for organizations to address mistreatment proactively, even in the absence of formal complaints.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".