Once, twice, or three times as harmful? Ethnic harassment, gender harassment, and generalized workplace harassment.
Bibliographic record
Abstract
Despite scholars' and practitioners' recognition that different forms of workplace harassment often co-occur in organizations, there is a paucity of theory and research on how these different forms of harassment combine to influence employees' outcomes. We investigated the ways in which ethnic harassment (EH), gender harassment (GH), and generalized workplace harassment (GWH) combined to predict target individuals' job-related, psychological, and health outcomes. Competing theories regarding additive, exacerbating, and inuring (i.e., habituating to hardships) combinations were tested. We also examined race and gender differences in employees' reports of EH, GH, and GWH. The results of two studies revealed that EH, GH, and GWH were each independently associated with targets' strain outcomes and, collectively, the preponderance of evidence supported the inurement effect, although slight additive effects were observed for psychological and physical health outcomes. Racial group differences in EH emerged, but gender and race differences in GH and GWH did not. Implications are provided for how multiple aversive experiences at work may harm employees' well-being.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".