Tobacco industry tactics in preparing for menthol ban
Bibliographic record
Abstract
Research reported in this publication was supported by the National Institute on Drug Abuse of the National Institutes of Health under Award Number F3667. The US Food and Drug Administration concluded that a ban on menthol cigarettes would likely elicit a reduction in cigarette consumption, increased cessation and reduced initiation of smoking.1 Understanding how the tobacco industry prepared for a menthol ban in Ontario, Canada—a province with some 2 million smokers—can be useful to jurisdictions preparing similar bans. One previous menthol ban study2 found menthol replacement packs with the word ‘menthol’ replacing the word ‘green’ and with cellophane wrappers with the wording ‘smooth taste (redesigned) without menthol’. Ontario’s ban on the sale of menthol cigarettes, first announced in May 2015, took effect on 1 January 2017. As the first phase of a pre–post ban study that aims to understand changes in cigarette packaging and product in response to the menthol …
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.044 | 0.043 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".