International Tobacco Litigation’s Evolution as a United States Torts Law Export: To Canada and Beyond?
Bibliographic record
Abstract
In the late 1990’s, the states’ healthcare reimbursement lawsuits against the tobacco industry were settled for approximately $246 billion. In the wake of this enormous settlement, many similar lawsuits were initiated in other nations or by other nations. Most of these early healthcare reimbursement lawsuits failed. However, in 2005, the World Health Organization Framework Convention on Tobacco Control was finalized by over 150 nations, and today has been ratified by 168 nations. The Framework encourages nations to consider tort litigation against tobacco sellers as a way to limit tobacco usage. Canada’s provinces have been particularly aggressive in seeking to use healthcare reimbursement lawsuits inspired by the United States litigation as a tool for tobacco control. This Article considers ways in which United States-style litigation against tobacco companies might be both helpful and hurtful for other nations.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.025 | 0.024 |
| Scholarly communication | 0.029 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".