Tobacco Endgame Strategies: Challenges in Ethics and Law
Bibliographic record
Abstract
There are complex legal and ethical tradeoffs involved in using intensified regulation to bring smoking prevalence to near-zero levels. The authors explore these tradeoffs through a lens of health justice, paying particular attention to the potential impact on vulnerable populations. The ethical tradeoffs explored include the charge that heavy regulation is paternalistic; the potentially regressive impact of heavily taxing a product consumed disproportionately by the poor; the simple loss of enjoyment to heavily addicted smokers; the health risks posed by, for example, regulating nicotine content in cigarettes—where doing so leads to increased consumption. Turning to legalistic concerns, the authors explore whether endgame strategies constitute a form of ‘regulatory taking’; whether endgame strategies can be squared with global trade/investment laws; whether free speech rights are infringed by aggressive restrictions on the advertisement and marketing of cigarettes.
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 | 0.037 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.064 |
| Scholarly communication | 0.024 | 0.019 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.028 | 0.027 |
| Insufficient payload (model declined to judge) | 0.004 | 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".