The role of public law-based litigation in tobacco companies’ strategies in high-income, FCTC ratifying countries, 2004–14
Bibliographic record
Abstract
BACKGROUND: Tobacco companies use a host of strategies to undermine public health efforts directed to reduce and eliminate smoking. The success, failure and trends in domestic litigation used by tobacco companies to undermine tobacco control are not well understood, with commentators often assuming disputes are trade related or international in nature. We analyse domestic legal disputes involving tobacco companies and public health actors in high-income countries across the last decade to ascertain the types of action and the success or failure of cases, develop effective responses. METHODS: WorldLii, a publicly available online law repository, was used to identify domestic court cases involving tobacco companies from 2004 to 2014, while outcome data from LexisNexis and Westlaw databases were used to identify appeals and trace case history. RESULTS: We identified six domestic cases in the UK, Australia and Canada, noting that the tobacco industry won only one of six cases; a win later usurped by legislative reform and a further court case. Nevertheless, we found cases involve significant resource costs for governments, often progressing across multiple jurisdictional levels. DISCUSSION: We suggest that, in light of our results, while litigation takes up significant time and incurs legal costs for health ministries, policymakers must robustly fend off suggestions that litigation wastes taxpayers' money, pointing to the good prospects of winning such legal battles.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".