Illegal Experimental Tobacco Marketplace II: effects of vaping product bans — findings from the 2020 International Tobacco Control Project
Bibliographic record
Abstract
SIGNIFICANCE: Restrictive e-cigarette policies may increase purchases from illegal sources. The Illegal Experimental Tobacco Marketplace (IETM) allows examination of how restrictions impact illegal purchases. We investigated (1) the effect of a vaping ban, total flavour vaping ban and partial flavour vaping ban on the probability of purchasing illegal vaping products among different regulatory environments (USA, Canada and England) and tobacco user types (cigarette smokers, dual users and e-cigarette users); and (2) the relation between ban endorsement and illegal purchases. METHODS: Participants (N=459) from the International Tobacco Control Survey rated their support of bans and chose to purchase from a hypothetical legal experimental tobacco marketplace or IETM under control and the three ban conditions. RESULTS: In total, 25% of cigarette smokers, 67% of dual users and 79% of e-cigarette users made IETM purchases. Cross-country comparisons depicted dual users from Canada (OR: 19.8), and e-cigarette users from the USA (OR: 12.9) exhibited higher illegal purchases odds than the same user type in England. Within-country comparisons showed e-cigarette and dual users are more likely to purchase from the IETM than cigarette smokers in the most restrictive condition, with the largest effects in e-cigarette users (England-OR: 1722.6, USA-OR: 22725.3, Canada-OR: 6125.0). Increased opposition towards partial or total flavour ban was associated with increased IETM purchasing in the corresponding condition. CONCLUSIONS: Vaping restrictions may shift users' preference to the illegal marketplace in a regulatory environment. Evidence of the IETM generalisability in a geographically dispersed sample enhances its utility in tobacco regulatory science.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".