Cyber‐Vetted Behind the Smokescreen: The Evaluations of Cigarette and Cannabis Smokers in Hiring
Bibliographic record
Abstract
ABSTRACT A history of cannabis prohibition and tobacco/drug‐control campaigns has created negative stereotypes around cigarette and cannabis users. Cyber‐vetting, where organizations scan prospective employees’ online footprints, has also risen. This research integrates stereotyping and personnel selection literatures to examine whether cyber‐vetted knowledge of job applicants’ private smoking behaviors influences assessor evaluations regardless of interview performance. It also examines the implications of when cyber‐vetting takes place. Three experiments with student and professional samples paired social media cyber‐vetting with realistic video‐based interview simulations in North American jurisdictions where recreational cannabis is legal. In Study 1, 224 Canadian business students, role‐playing as hiring managers, gave lower ratings to cigarette and cannabis smokers. A high‐quality interview, while helpful, did not overcome the lowered evaluations. In Study 2 (with 318 Canadian business students), we used the Stereotype Content Model to show the indirect impact of smoking status on expected counterproductive workplace behaviors and final evaluations via diminished competence and warmth perceptions. In Study 3, 185 HR professionals in California also rated smokers unfavorably. While overall evaluations were higher when cyber‐vetting occurred pre‐ versus post‐interview for cannabis smokers, no significant differences were found for cigarette smokers. Overall, we highlight implications for smokers’ employability as cyber‐vetting and legal access to cannabis both gain traction.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".