White and minority employee reactions to perceived discrimination at work: evidence of White fragility?
Bibliographic record
Abstract
Purpose Building on the notion of “White fragility,” this study aims to explore how Whites react and cope with perceived discrimination at work. Specifically, the authors explore whether: (1) Whites react more negatively than minorities when they perceive discrimination at work and (2) Whites are more likely than minorities to restore the status quo by leaving the situation when they perceive discrimination at work. Design/methodology/approach Data for this study were obtained from the Professional Worker Career Experience Survey. In total, 527 working professionals from multiple organizations across the central USA participated in the survey. Findings The authors find evidence that Whites experience more negative psychological effects (i.e. lower job satisfaction and higher work stress) from perceived discrimination than minority employees and are more likely to act to restore conditions of privilege by leaving their current job and employer. The stronger negative effects of perceived discrimination for Whites (vs minorities) were restricted to work outcomes (job satisfaction, work stress, turnover intentions from one's employer) and were not evident with respect to perceptions of overall well-being (i.e. life satisfaction), suggesting that White fragility may play a particularly influential role in work settings, wherein racial stress may be more readily activated. Originality/value Consistent with the notion of White fragility, the study’s results demonstrate that the deleterious impact of perceived discrimination on employee work outcomes may, in some cases, be stronger for White than minority employees.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".