Bibliographic record
Abstract
Abstract After toying with the idea of restrictions in the early 20th century, Canadian governments only began developing policies to reduce smoking in the 1960s after the link between smoking and cancer was firmly established. Half a century later, they are still at it. The comprehensive set of tobacco control policies implemented in Canada, and now embraced by a global tobacco treaty, has focused on reducing the demand for tobacco products. Early approaches in the 1960s and 1970s relied on public education about the health risks and encouraging smokers to quit. By the mid 1980s, the insufficiency of this programmatic approach was recognized, and governments moved to impose and progressively tighten regulatory controls on marketing. Once ubiquitous promotions, like billboards, retail displays, sponsored events, and colourful packages were removed from the Canadian social environment. Measures to protect citizens from the harms of second-hand smoke began in the mid 1970s and, by 2010, to clean the air of workplaces and public venues, decreasing the social acceptability of smoking. Tobacco taxes, once used purely to generate revenues for the government, became recognized as a powerful public health tool to deter use with higher prices. The success of this set of demand-reduction policies can be measured in the reduction in the percentage of Canadians who smoke—from one-half of adults in the mid-1960s to about one-sixth today. The limits are seen in the industry’s continued ability to recruit new smokers to replace those who quit and die.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.009 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.018 |
| Insufficient payload (model declined to judge) | 0.026 | 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".