Tobacco excise taxes: a health and social justice measure?
Bibliographic record
Abstract
As Verguet et al note,1 taxing tobacco products has been consistently shown to reduce smoking prevalence by stimulating cessation, deterring uptake and reducing consumption among people who continue to smoke.2–6 Health benefits attributable to tobacco excise tax increases include increased life expectancy and reduced hospitalisations.7 8 Tobacco excise taxes can potentially bring large health benefits at a population level,9 particularly for young people and people with fewer financial resources.3 10 Yet tobacco taxation in most countries is low; in 2014 experts estimated that 200 million deaths could be averted by 2025 if the price of cigarettes was doubled globally, which ‘in many low and middle-income countries’ could be achieved by tripling the tax on tobacco.11 This evidence has led many countries, including the UK, Ireland, France and Canada, to implement regular tobacco excise tax increases. Australia and New Zealand have taken this policy further than other countries; sustained increases in tobacco excise taxes mean a pack of 20 cigarettes now costs around $35 (approximately USD20) in New Zealand and will soon reach $40 per pack (around USD25) in Australia. The people most likely …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.008 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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; both teacher heads agree on what is shown here.
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".