Bibliographic record
Abstract
OBJECTIVE: This paper describes secondhand smoke (SHS) litigation over the past quarter century where non-smoking litigants have prevailed and attempts to decipher trends in the law that may impact the course of future cases. METHODS: Since the early 1980s, the author has sought and examined legal cases in which SHS exposure is an important factor. Law library searches using the official reporter system (for example, Shimp v. New Jersey Bell Telephone Co., 368 A.2d 408) have more recently been combined with computerised online searches using LexisNexis and Westlaw. The author has learned of other cases through personal correspondence and from articles in newspapers. Over 420 cases involving exposure to SHS were identified. Each case was reviewed and summarised. RESULTS: Since 1976, the year of the first reported SHS lawsuit, this type of litigation has increased both in number and in scope with increasing success. While it is common for initial cases to lose in a new area where the law eventually evolves, litigants and their lawyers who later bring similar cases can learn from those previous, unsuccessful cases. It is now apparent that the judicial branch has begun to recognise the need to protect the public-especially some of the most vulnerable members of our society-from the serious threat to their health that is exposure to SHS. CONCLUSIONS: Successful cases brought on behalf of individuals exposed to SHS produce an additional benefit for the public health by both paving the way for other non-smoking litigants to succeed in their cases and persuading business owners and others voluntarily to make their facilities 100% smoke-free.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".