Bibliographic record
Abstract
The past two decades have seen growing calls for the "tobacco endgame." Its advocates are united by their commitment to two ideas. First, tobacco-related harms represent a catastrophic health emergency, and second, current tobacco-control approaches are an inadequate response to the scale of that emergency. To endgame advocates, tobacco policy should have more ambitious goals than merely "controlling" tobacco. Instead, it should aim to bring about a smoke-free world. While a range of different policies are included under the umbrella of the "tobacco endgame," the most radical proposal is for a complete ban on tobacco. Its advocates argue that in addition to improving global public health, an effective ban on tobacco would also promote overall autonomy and would have important egalitarian benefits. This article critically examines these arguments for a tobacco ban. I argue that they rely on idealizing assumptions about the likely effects of a ban. Because an effective ban would require robust enforcement to control the illegal market in tobacco, it would be more likely to undermine autonomy and equality than it would be to promote them. By relying on idealizing assumptions and ignoring the likely consequences of a tobacco ban, advocates of a ban obscure, rather than clarify, both the policy debate and the ethical stakes. I conclude by considering the ways that idealizing assumptions should-and should not-play a role in debates about ethical issues in public policy.
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 | 0.046 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.077 |
| Scholarly communication | 0.023 | 0.025 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.028 | 0.035 |
| Insufficient payload (model declined to judge) | 0.006 | 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".