British Columbia capital regional district 100% smokefree bylaw: a successful public health campaign despite industry opposition: Table 1
Bibliographic record
Abstract
OBJECTIVE: To describe how the British Columbia Capital Regional District successfully passed, implemented, and enforced a 100% smokefree bylaw in all public places, including restaurants and bars, despite an aggressive campaign by the tobacco industry (acting through the hospitality industry) to stop it. METHODS: Information was obtained from news reports, internal tobacco industry documents, reports, public documents, and interviews with key players. Tobacco industry documents were accessed between February and April 2002. This project was approved by the University of California San Francisco committee on human research. RESULTS: As in the USA and elsewhere in the world, the tobacco industry in British Columbia, Canada, recruited and created hospitality associations to fight against the district smokefree bylaw. They used the classic industry rhetoric of individual rights and freedoms, economic devastation, and ventilation as a solution. Public health authorities were able to counter industry strategies with a strong education campaign, well written bylaws, and persistent enforcement. CONCLUSION: It is possible to overcome serious opposition orchestrated by the tobacco industry and develop and implement a 100% smokefree bylaw in Canada. Doing so requires attention to detail in drafting the bylaw, as well as a public education campaign on the health dangers of secondhand smoke and active enforcement to overcome organised resistance to the bylaw. Jurisdictions considering smokefree bylaws should anticipate this opposition when developing and implementing their bylaws.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".