Short-Interval Cortical Inhibition and Rhetoric and the Law, Or the Law of Rhetoric: How Countries Oppose Novel Tobacco Control Measures at The World Trade Organization
Bibliographic record
Abstract
The tobacco industry has developed an extensive array of strategies and arguments to prevent or weaken government regulation. These strategies and arguments are well documented at the domestic level. However, there remains a need to examine how these arguments are reflected in the challenges waged by governments within the World Trade Organization (WTO). Decisions made at the WTO have the potential to shape how countries govern. Our analysis was conducted on two novel tobacco control measures: tobacco additives bans (Canada, United States and Brazil) and plain, standardized packaging of tobacco products (Australia, New Zealand, Ireland, EU and UK). We analyzed WTO documents (i.e. meeting minutes and submissions) (n = 62) in order to identify patterns of argumentation and compare these patterns with well-documented industry arguments. The pattern of these arguments reveal that despite the unique institutional structure of the WTO, country representatives opposing novel tobacco control measures use the same non-technical arguments as those that the tobacco industry continues to use to oppose these measures at the domestic level.
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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 | 0.012 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".