Intersectional Penalties for Perceived Interpersonal Justice Violations among Black and Hispanic Male Leaders in the Workplace
Bibliographic record
Abstract
The Black Lives Matter (BLM) movement has drawn attention to the lack of progress toward racial equity in many domains. Chief among them is the unequal treatment that Black men often face when interacting with law enforcement or within the criminal justice system, which appears heavily associated with the pernicious stereotype that Black men are distinctly aggressive and dangerous. Evidence suggests that Hispanic men are also subject to similar negative stereotypes. We contend that the consequences of this intersectional stereotype are wide-ranging and explore how it manifests and continues to shape the experiences of Black and Hispanic men in contemporary work organizations. Across two field studies surveying employees supervised by a diverse set of leaders, we find evidence that leaders' intersectional identities moderate the relationship between interpersonal injustice and leader evaluations (i.e., performance ratings, reward recommendations) and relational outcomes (i.e., supervisor-directed organizational citizenship behaviors), such that Black or Hispanic men are penalized more severely for violations of interpersonal justice relative to White men as well as Black or Hispanic women. Additionally, this unequal response across leaders is because subordinates find such aggressive actions less acceptable for Black or Hispanic men, as it violates societal proscriptions surrounding for whom aggressive behaviors are deemed acceptable, rather than due to greater fear associated with the content of this negative stereotype. Supplementary Information: The online version contains supplementary material available at 10.1007/s10869-024-09994-z.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.007 | 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".