Are We under the Influence of What This Study See: The Power of Body Tattoos in a Job Interview
Bibliographic record
Abstract
Fair treatment of employees is an important ethical question. With the increasing number of tattooed workers in the workplace and on the job market, their work experience and the treatment they receive deserve more attention. To date, however, very few studies have focused on such a niche group of employees. The intention of this study was to offer insights on the experience of tattooed individuals in job interviews. Using an experimental design, this study examined the influence of visible tattoos on hiring decisions and interviewers’ evaluations. Participants (N=233) were recruited online, in China, and they were assigned to one of four experimental conditions: tattoo vs. no tattoo job applicant and entry-level vs. managerial positions. The results show that applicants with visible tattoos had decreased chances of being hired. Interviewers in the study were also more likely to perceive the virtual job candidate with tattoos as less competent, especially when hiring at the management-level position. These results serve to raise awareness around biases and stereotypes experienced by tattooed individuals seeking employment.
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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 | 0.058 | 0.202 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.012 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".