Why Do Racial Slurs Remain Prevalent in the Workplace? Integrating Theory on Intergroup Behavior
Bibliographic record
Abstract
Racial slurs are prevalent in organizations; however, the social context in which racial slurs are exchanged remains poorly understood. To address this limitation, we integrate three intergroup theories (social dominance, gendered prejudice, and social identity) and complement the traditional emphasis on aggressors and targets with an emphasis on observers. In three studies, we test two primary expectations: (1) when racial slurs are exchanged, whites will act in a manner more consistent with social dominance than blacks; and (2) this difference will be greater for white and black men than for white and black women. In a survey (n = 471), we show that whites are less likely to be targets of racial slurs and are more likely to target blacks than blacks are to target them. We also show that the difference between white and black men is greater than the difference between white and black women. In an archival study that spans five years (n = 2,480), we found that white men are more likely to observe racial slurs than are black men, and that the difference between white and black men is greater than the difference between white and black women. In a behavioral study (n = 133), analyses showed that whites who observe racial slurs are more likely to remain silent than blacks who observe slurs. We also find that social dominance orientation (SDO) predicts observer silence and that racial identification enhances the effect of race on SDO for men, but not for women. Further, mediated moderation analyses show that SDO mediates the effect of the interaction between race, gender, and racial identification on observer silence.
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.
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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".