Identical applicant but different outcomes: The impact of gender versus race salience in hiring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it