The Stigma of Workplace Mistreatment and How to Lessen It
Bibliographic record
Abstract
Workplace mistreatment is a pervasive issue that has a host of deleterious consequences for those who experience it. While scholars have suggested that observers can help alleviate negative consequences of mistreatment by supporting targets, recent meta-analyses show that this is not always the case. We suggest that one of the reasons for negative observer reactions toward targets of mistreatment may lie in the potential of the mistreatment incident to cast a ‘mark’ on the target. Drawing on stigma theory, we posit that mistreatment can be a source of stigma for the targets, which, in turn, can prompt observers to react in ways that signal that they are not like and/or not affiliated with the target. Integrating insights from destigmatization literature, we further examine the role of target responses in countering the stigmatizing effects of mistreatment. Across three studies, we explore the content of stigma, the stigmatizing potential of mistreatment and its downstream consequences (e.g., target avoidance, negative gossip about the target), and the role of target responses in lessening stigma. From a theoretical perspective, our research illuminates identity-related implications of experienced mistreatment. Most importantly, from a practical perspective, our findings shed light on the role of target responses in alleviating these negative effects.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".