Class Actions, Punitive Damages, and Decreasing Consumption of Tobacco Products
Bibliographic record
Abstract
Abstract: In 2015, the Superior Court of Quebec awarded over $15 billion in damages, including $1.6 billion in punitive damages, in a class action against three major tobacco companies. This judgment is significant as it represents the first successful class action against a tobacco company in Canada. In light of the Superior Court judgment, this essay explores whether awards of punitive damages against tobacco companies in class action proceedings can play a role in decreasing demand for tobacco products. It will be argued that punitive damages awards, especially in the context of large class actions, can be a powerful complementary tool to advance regulatory efforts to decrease tobacco consumption. Tobacco is a unique product, in that it is an inherently dangerous product with no safe consumption level that is lawfully sold, thus requiring a unique regulatory response. At the domestic and international level, the response has been an indirect ban. Regulators have used the concepts of de-normalization and consumer empowerment to reduce the demand for tobacco products, with the hopes of reaching a zero-consumption level. Similarly, awards of punitive damages can contribute to the de-normalization of the tobacco industry by shedding light on the misconduct of tobacco companies. Punitive damages awarded in class action proceedings can also increase access to justice, provide financial incentives, and restore power inequalities between merchants and consumers, resulting in consumer empowerment. In doing so, awards of punitive damages contribute to curtailing demand for tobacco products and go hand in hand with the current regulatory approach.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".