Placing the Legal Vape Market in the Hands of Big Tobacco
Bibliographic record
Abstract
Placing the Legal Vape Market in the Hands of Big Tobacco Adam R. Houston PhD candidate, Amelia Howard PhD candidate, and David Sweanor JD Affiliation Adam R. Houston is a PhD candidate in the Faculty of Law, University of Ottawa, Ottawa, ON, Canada. Amelia Howard is a PhD candidate in the Department of Sociology and Legal Studies, University of Waterloo, Waterloo, ON, Canada. David Sweanor is with the Centre for Health Law, Policy and Ethics, University of Ottawa.CopyRightCorrespondence should be sent to David Sweanor, c/o Centre for Health Law, Policy and Ethics, 57 Louis Pasteur (Fauteux Hall), Ottawa, Ontario K1N 6N5, Canada (e-mail: dsweanor@uottawa.ca). Reprints can be ordered at http://www.ajph.org by clicking the “Reprints” link.CONTRIBUTORSA. R. Houston wrote the initial draft in consultation with A. Howard and D. Sweanor. All authors revised and finalized the comment. https://doi.org/10.2105/AJPH.2020.305676 Accepted: March 16, 2020 Published Online: May 06, 2020
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.020 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".