Supply-side options for an endgame for the tobacco industry
Bibliographic record
Abstract
Although governments have imposed controls on tobacco company behaviour, they have not yet aligned tobacco industry goals to public health objectives. As a result, tobacco companies have delayed or diminished the impact of imposed public health measures and have not contributed to curbing the epidemic of tobacco use. Over the past decade, several regulatory innovations have been proposed as ways to better align industry actions with public health needs, but none have been put in place. These policy suggestions share the goal of providing a supply-side complement to conventional demand reduction strategies, but they differ in the assumptions they make and in the regulatory and governance approaches they take. Similarly, differing views on ideology and political context within the tobacco control community and between governments may hinder the establishment of a global consensus on the ideal supply-side intervention. A government willing to implement innovative supply-side strategies as part of a tobacco control endgame may not require such consensus if factors specific to their national public health systems or political contexts are supportive.
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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".