Is the Cowboy Killer also an Employment Opportunity Killer? Reactions to Smokers in Selection
Bibliographic record
Abstract
The negative health consequences of tobacco consumption have long been known, and decades of tobacco control initiatives have increasingly turned public sentiment against smoking. Initial anti-smoking campaigns—centered on adverse health warnings and restrictions on cigarette advertising—that followed the first warning issued by the US Surgeon General in 1964 were deemed as inadequate. These gradually gave way to rhetoric that framed smoking as hazardous to others and smokers as deviant and undesirable. The social transformation of smokers from glamorous to stigmatized, while lowering smoking rates, has also come at a cost: smokers have lost social status and are discriminated against in a variety of domains including the workplace. This research empirically examines reactions to smoking behaviors of job applicants within the hiring process. Results of a laboratory experiment indicate that participants evaluate applicants as significantly less qualified on job-related attributes (e.g., communication skills, teamwork, and conscientiousness), and experience more negative emotional responses (e.g., anger and disgust) when applicants are smokers as compared to control candidates, even when the effect of participants’ own smoking-related attitudes is controlled for. More failure-oriented questioning of smokers (that aims to throw them off-guard) is also engaged in. These findings suggest the presence of discriminatory biases against smokers within hiring decisions. Implications of the lack of objective assessments of job applicant suitability are discussed from the perspectives of applicants as well as the organizations that seek to employ them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".