Bibliographic record
Abstract
During the past month alone, I have had an opportunity to participate in four different meetings—in Utah, Sweden, Canada and the USA—where ideas about how to achieve an ‘endgame’ for the tobacco epidemic were being discussed. Other previous meetings—in India, Finland and the UK—have been held over the last few years, and I know of several other such discussions. Even before the US Surgeon General's 50th Anniversary report on the health consequences of smoking1 called explicitly for achieving a tobacco endgame and suggested a combination of policy strategies to do so, other countries such as Ireland, New Zealand, Finland and Scotland were having conversations that led them to set hard target dates by which they intend to reduce tobacco use and/or smoking prevalence to <5%. Modelling studies suggest that current measures, even if they are greatly accelerated in countries that are tobacco control leaders, will not achieve these goals within the first half of this century.1 As the target dates move closer, will political leaders seize the opportunity to enact the bolder policy innovations that must be undertaken? Or, will they allow caution and inertia to shape another century of public health catastrophe? The endgame requires consciously designing interventions to change permanently the structural, political and social dynamics that sustain the epidemic, in order to end it by a specific time.2 Thus, an endgame vision goes beyond ‘business as usual’ and calls for further policy innovations. No one knows how to do it yet, but the endgame conversation3–6 has become mainstream, and the first places that manage to achieve it will herald the beginning of the end of more than a century of industrially produced carnage. Which places will be …
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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 | 0.014 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.016 | 0.022 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.019 | 0.034 |
| Insufficient payload (model declined to judge) | 0.037 | 0.006 |
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".