‘Both Sides of the Argument’? A critical review of existing evidence on the illicit trade in tobacco products in Canada
Bibliographic record
Abstract
INTRODUCTION: The illicit trade in tobacco products (ITTP) is widely recognised as a substantial and complex problem in Canada. However, the independence of available data and quality of analyses remains unknown. Reliable and accurate data on the scale and causes of the problem are needed to inform effective policy responses. METHODS: We searched the scholarly and grey literature using keywords related to ITTP in Canada. We identified 26 studies published in English since 2008 that present original research drawing on primary data. We analysed these studies for their independence from the tobacco industry, methodology, findings and gaps in knowledge. RESULTS: The study finds 42% of the literature reviewed has links to the tobacco industry. These studies provide insufficient methodological detail, present higher estimates of the volume of ITTP and attribute the causes to higher rates of tobacco taxation. The classification of all indigenous tobacco sales as illicit, by both industry linked and independent studies, contributes to overestimates and serves the interests of transnational tobacco companies. There is need for independent and comprehensive data on the ITTP in Canada over time, across population groups and geographies. CONCLUSION: While there is evidence that the ITTP in Canada is a major and complex issue that requires effective tobacco control policies, there is a limited evidence base on which to develop such responses. This review finds industry-linked studies lack independence, employ biased methodologies and serve tobacco industry interests. Independent studies present more rigorous approaches, but primarily focus on youth and the province of Ontario.
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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 | 0.044 | 0.132 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.033 | 0.043 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".