Black or White? It depends where you work: organization type influences how a racially ambiguous person is racially categorized
Bibliographic record
Abstract
Humans regularly categorize one another based on race, with these categorizations influencing how people are viewed and treated. Racial categorizations are not always accurate, however, especially for those who are multiracial. We examined how information about a racially ambiguous man’s occupation (specifically, the person’s role [leader vs. employee] and the type of organization [Fortune 500 company vs. non-profit]) may influence how they are racially categorized and potential downstream consequences. Although relatively weak in one study but stronger in the other, a racially ambiguous man working in a Fortune 500 (vs. non-profit) was perceived as relatively more White. This pattern was present for participants higher in social dominance orientation. Findings regarding downstream consequences were less clear but suggested that when a racially ambiguous man was viewed as more White, they were evaluated less positively by those lower in social dominance orientation, possibly due to perceptions that the man did not obtain their position based on individual merit.
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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 | 0.005 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".