Social norms and smoking bans on campus: interactions in the Canadian university context
Bibliographic record
Abstract
Smoking bans offer practical protection against environmental tobacco smoke and highlight the decreasing normative status of smoking. At Canadian universities, indoor smoking is now completely prohibited, but regulations vary with respect to outdoor smoking. The purpose of this research was to conceptualize the interactions of smoking bans on campus with changing social norms around smoking. Interviews were conducted with 36 key informants, exploring the development and normative significance of smoking bans at three case study institutions. Five key themes were identified in the transcripts. First, universities were understood as community leaders and role models. Second, they were viewed as institutions with a mandate to promote health. Third, students were generally perceived to view smoke-free environments and lifestyles as normative. Fourth, respondents also acknowledged that students remain vulnerable to social and behavioural influences that can encourage smoking. Finally, they articulated bans' normative effects: restricting where smoking occurs on campus may discourage initiation and support cessation. Our findings suggest that health-promoting policies, such as smoking bans, can be motivated by changes in social norms and that their implementation reinforces this norm shift. Moreover, the contextual and compositional characteristics of universities mean they are uniquely placed to adopt such initiatives.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".