Diversity climate affords unequal protection against incivility among Asian workers: The COVID‐19 pandemic as a racial mega‐threat
Bibliographic record
Abstract
Abstract Despite longstanding recognition that organisations are open systems that are affected by the broader environments in which they are situated, scholars have rarely examined how such macrosocietal conditions may influence processes and experiences within the workplace. Integrating research on selective incivility and mega‐threats, we conceptualise the COVID‐19 pandemic as a racial mega‐threat and examine how this context may challenge organisations' efforts to promote diversity and inclusion. Specifically, we predict that the protective benefits of diversity climate against incivility, an insidious form of modern discrimination incited by the COVID‐19 pandemic, will be weaker for workers of Chinese descent compared to workers from other Asian subgroups, leading to more downstream negative outcomes for this group of workers (i.e., higher turnover intentions, poorer job performance and greater emotional exhaustion). This reflects the fact that workplaces are not impervious to the rising xenophobia toward China and Chinese people, who were particularly blamed and stigmatised for the emergence of this virus, as evident in North American society in early 2020. We found support for our predictions in a three‐wave, time‐separated study of Asian workers ( N = 248) in the US and Canada during the first wave of the pandemic.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".