Examining the impact of applicant smoking and vaping habits in job interviews
Bibliographic record
Abstract
Cigarette and electronic-cigarette users (i.e. vapers) are increasingly stigmatized in both society and the workplace. We examine effects of this stigmatization in the selection process by testing whether interviewers’ negative initial impressions of smokers and vapers extend throughout the interview. We used a dual-process framework of interviewer bias against stigmatized applicants, comprised of Type I-automatic and Type II-systematic processes, and conducted two experiments where US and Canadian participants enacted the role of an interviewer in video-based job interview simulations. Consistent with Type I processes, results show that cigarette smokers, and to lesser extent vapers, were initially rated as less qualified than non-smokers. These initial impressions were not subjected to justification/rationalization during the interview via harder questions asked. However, they served as anchors, also consistent with Type I processes, and impacted final assessments alongside Type II adjustments based on applicants’ response quality. Additionally, using attentional eye tracking data, we found that raters with worse attitudes toward smoking, but not vaping, glanced at stigma cues more frequently, which went on to influence first impressions. These findings provide valuable tests of key components of the dual-process model of interviewer bias, and raise concerns around the devaluation of smokers and vapers in hiring decisions.
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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 | 0.038 | 0.114 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".