Vape Club: Exploring Non-Profit Regulatory Models for the Supply of Vaporised Nicotine Products
Bibliographic record
Abstract
Vaporised nicotine products (VNPs) that are not approved as therapeutic goods are banned in some countries, including Australia, Singapore, and Thailand. We reviewed two non-profit regulatory options, private clubs and the Australian Therapeutic Goods Administration Special Access Scheme (SAS) that have been applied to other controlled substances (such as cannabis) as a potential model for regulating VNPs as an alternative to prohibition. The legal status of private cannabis clubs varies between the United States, Canada, Belgium, Spain, and Uruguay. Legal frameworks exist for cannabis clubs in some countries, but most operate in a legal grey area. Kava social clubs existed in the Northern Territory, Australia, until the federal government banned importation of kava. Access to medical cannabis in Australia is allowed as an unapproved therapeutic good via the SAS. In Australia, the SAS Category C appears to be the most feasible option to widen access to VNPs, but it may have limited acceptability to vapers and smokers. The private club model would require new legislation but could be potentially more acceptable if clubs were permitted to operate outside a medical framework. Consumer and regulator support for these models is currently unknown. Without similar restrictions applied to smoked tobacco products, these models may have only a limited impact on smoking prevalence. Further research could explore whether these models could be options for regulating smoked tobacco products.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".