Prevalence and correlates of purchasing contraband cigarettes on First Nations reserves in Ontario, Canada
Bibliographic record
Abstract
AIMS: Non-First Nations people purchasing cigarettes on First Nations reserves do not pay applicable taxes. We estimated prevalence and identified correlates of purchasing contraband cigarettes on reserves; we also quantified the share of contraband purchased on reserves relative to reported total cigarette consumption and the associated financial impact on taxation revenue. DESIGN: Data from the Ontario Tobacco Survey, a regionally stratified representative population telephone survey that over-samples smokers. SETTING: Ontario, Canada. PARTICIPANTS: A total of 1382 adult current smokers. MEASUREMENTS: Reported status of purchasing cigarettes on reserves and the quantity of cigarettes bought on reserves. The prevalence of purchasing cigarettes on reserves was assessed with descriptive statistics. A two-part model was used to analyse correlates of having recently purchased contraband. FINDINGS: A total of 25.8% reported recent purchasing and 11.5% reported usual purchasing. Heavy smoking, having no plans to quit and lower education were correlated with recent purchasing. Heavy smoking and not having plans to quit were also correlated with buying more packs of cigarettes on reserves. Contraband purchases on reserves accounted for 14.0% of the reported total cigarette consumption and resulted in an estimated tax loss of $122.2 million. CONCLUSIONS: There was substantial purchasing of contraband cigarettes on reserves in Ontario, resulting in significant losses in tax revenues. The availability of these cheap cigarettes undermines the effectiveness of tobacco taxation to reduce smoking. Wherever indicated, governments should strengthen their contraband prevention and control measures, as recommended by the Framework Convention on Tobacco Control, to ensure that tobacco taxation achieves its intended health benefits and that tax revenues are protected.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".