Identical applicant but different outcomes: The impact of gender versus race salience in hiring
Bibliographic record
Abstract
People belong to multiple social groups, which may have conflicting stereotypic associations. A manager evaluating an Asian woman for a computer programming job could be influenced by negative gender stereotypes or by positive racial stereotypes. We hypothesized that evaluations of job candidates can depend upon what social group is more salient, even when both are apparent. In three studies, using student (Study 1) and nonstudent (Studies 2 and 3) samples, we compared ratings of an Asian American female applicant after subtly making her race or gender salient in stereotypically male employment contexts. Consistent with our predictions, we found evidence that men rated her as more skilled (Studies 1 and 3), more hirable (Studies 1–3), and offered her more pay (Study 2) in science and technology-related positions when her race, rather than gender, was salient. The theoretical implications for person perception and practical implications in employment contexts are discussed.
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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 | 0.006 | 0.021 |
| 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".