authors ’ affiliations Correspondence to:
Bibliographic record
Abstract
Objective: Post-secondary institutions provide a unique opportunity to implement and evaluate leading edge tobacco policies, while influencing a key group of young adults. To date, however, we know little about the tobacco control environment at post-secondary institutions outside the USA. Design: Telephone surveys were conducted with campus informants from 35 post-secondary institutions in Canada to evaluate tobacco control policies and the presence of tobacco marketing on campus. Main outcome measures: Tobacco marketing on campus, tobacco control policies, and attitudes towards tobacco control. Results: The findings indicate that tobacco marketing is prevalent among post-secondary institutions in Canada. Every university and half of all colleges surveyed had participated in some form of tobacco marketing in the past year. Among universities, 80 % had run a tobacco advertisement in their paper and 18 % had hosted a tobacco sponsored nightclub event. Tobacco control policies varied considerably between institutions. Although several campuses had introduced leading edge policies, such as campus wide outdoor smoking restrictions and tobacco sales bans, there is a general lack of awareness of tobacco issues among campus decision makers and fundamental public health measures, such as indoor smoke-free policies, have yet to be introduced in many cases.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.706 | 0.252 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".