Experimental evidence of the prejudice and discrimination that exists towards tattooed people in some hiring processes
Bibliographic record
Abstract
A considerable body of research has shown having visible tattoos is linked to greater workplace-related discrimination, with some women reporting feeling dehumanized based on their tattoo status within the context of their careers. However, most of this work is qualitative in nature, and little correlational or experimental evidence has supported these links or examined specific mechanisms of prejudice that might underpin this discrimination. The present study addressed these gaps using an experimental design in which participants were told they would assist with hiring a research assistant by viewing and evaluating online video job applications. In one condition that applicant was tattooed, and in the other she was not. Results showed that participants were less likely to hire the tattooed target, and that this effect was specific to when participants were average or higher in their dehumanization of her. These findings held controlling for participant sex, own tattoo status, age, and hiring capability in their job. Together, these findings suggest that some tattooed job applicants may face implicit discrimination which is driven by dehumanization of the target.
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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 | 0.005 | 0.015 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".