Whitened Resumes: Race and Self-Presentation in the Labor Market
Bibliographic record
Abstract
Using interviews, a laboratory experiment, and a résumé audit study, we examine racial minorities’ attempts to avoid anticipated discrimination in labor markets by concealing or downplaying racial cues in job applications, a practice known as “résumé whitening.” Interviews with racial minority university students reveal that while some minority job seekers reject this practice, others view it as essential and use a variety of whitening techniques. Building on the qualitative findings, we conduct a lab study to examine how racial minority job seekers change their résumés in response to different job postings. Results show that when targeting an employer that presents itself as valuing diversity, minority job applicants engage in relatively little résumé whitening and thus submit more racially transparent résumés. Yet our audit study of how employers respond to whitened and unwhitened résumés shows that organizational diversity statements are not actually associated with reduced discrimination against unwhitened résumés. Taken together, these findings suggest a paradox: minorities may be particularly likely to experience disadvantage when they apply to ostensibly pro-diversity employers. These findings illuminate the role of racial concealment and transparency in modern labor markets and point to an important interplay between the self-presentation of employers and the self-presentation of job seekers in shaping economic inequality.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".