Tobacco industry response to menthol cigarette bans in Alberta and Nova Scotia, Canada
Bibliographic record
Abstract
Menthol cigarettes are associated with increased initiation and progression to regular smoking and decreased likelihood of smoking cessation.1–8 Menthol smokers are more likely to be women and adolescents in several countries.9 The Conference of the Parties to the Framework Convention on Tobacco Control recommend that Parties regulate ingredients that make cigarettes more palatable, including flavouring substances like menthol.10 The Canadian province Nova Scotia became the first jurisdiction to implement a ban on menthol tobacco products in May 2015, and the province of Alberta followed in September 2015.11 These regulations extended existing provincial bans on the sale of flavoured tobacco products to include menthol flavoured tobacco products, with the exception of pipe tobacco and some cigars. Additional Canadian provinces, Brazil, Ethiopia, Turkey and the European Union have passed regulations to ban menthol tobacco products.11 As jurisdictions (including cities, states/provinces and countries) consider bans on menthol tobacco products, real-life contextual data on the industry response to such bans can be helpful in formulating effective bans. For example, when misleading descriptors on tobacco packaging such as ‘light’ and ‘low tar’ were prohibited, the tobacco industry continued to communicate those same misleading health messages to the consumer using colour or other descriptors.12 ,13 Industry tactics to undermine the effectiveness of health warnings on tobacco packaging have included the use of promotional packaging and altered pack size.14 ,15 Drawing on a sample of cigarette packs …
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".