Once, twice, or three times as harmful? Ethnic harassment, gender harassment, and generalized workplace harassment.
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
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it