Evidence of Indirect Discrimination Against Black and Indigenous Men in a Simulated Hiring Scenario
Bibliographic record
Abstract
Racialized and criminalized men represent intersecting groups of marginalized workers. In simulated hiring scenario with 417 subjects, participants were randomly assigned to assess the résumé of a fictitious candidate who did or did not have a criminal record. Participants were asked to make a hiring decision for a position that the candidate was fully qualified to hold. We manipulated the names of the fictitious candidates to evoke the perception that the candidate was a White, Black, or Indigenous man. We found evidence of indirect discrimination based on an interaction between race and criminal record status, such that the presence of a criminal record was a greater disadvantage for racialized candidates. Content analyses found further support for indirect discrimination. Race was never reported as a reason for rejecting racialized candidates. Rather, racialized candidates were ostensibly rejected for other reasons, which were comparatively overlooked in White candidates: having a criminal record or being perceived to lack education, experience, or professionalism. The implications of the study underscore the need for affirmative action or quota-based hiring models, because even when overt discrimination is avoided, it can still manifest through indirect means.
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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 | 0.003 | 0.011 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".