Diversity Climate, Workplace Discrimination, and Voice Behavior: The Moderating role of SDO
Bibliographic record
Abstract
The present study aims at expanding research on the antecedents, consequences, and boundary conditions associated with perceived racial/ethnic discrimination in workplaces. We suggest that psychological climate for diversity relates to reduced perceptions of racial/ethnic discrimination, which in turn would relate to increased employee prosocial voice behavior. However, we theorize that these relationships would be moderated by employee social dominance orientation (SDO), an individual difference variable reflecting the degree to which individuals accept unequal distribution of power among social groups. Drawing upon two-wave survey data collected among 826 employees in Canada, psychological climate for diversity was found to negatively relate to the perception of racial/ethnic workplace discrimination. This relation was stronger among low-SDO employees. Moreover, the relation between perceived racial/ethnic workplace discrimination and prosocial voice behavior was moderated by employee SDO such that this relation was significantly positive among low-SDO employees but significantly negative among high-SDO employees. Further, the indirect effect of climate for diversity on prosocial voice was positive for high-SDO employees but negative for low-SDO employees. We discuss these findings in the context of a contingent view of the effects of diversity and discrimination perceptions in organizations, where employee SDO is a crucial boundary condition.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".