Fighting a tobacco rollback : a political analysis of the 1994 contraband crisis in Canada
Bibliographic record
Abstract
\n\t\t\t\t\tWe identify factors that led a regional government (Quebec, Canada) to opt for a reduction of its tobacco tax to combat tobacco smuggling. Then we explore the fallout of Quebec's tobacco-tax rollback on its tobacco control policy. We conducted qualitative research using a case-study design and multiple sources of data. We applied the Advocacy Coalition Framework in respect of data collection and analysis. Advocates of the tobacco-tax rollback framed the contraband problem in a way that won the support of an array of actors. However, anti-tobacco activists succeeded in convincing the government to invest more in tobacco control. The new resources were instrumental in enhancing the activists' ability to promote legislative measures. Our approach sheds light on the tobacco industry's strategy to have governments reducing their tobacco tax. Quebec offers an example of how tobacco control activists can transform defeat into the cornerstone of a comprehensive tobacco control policy. \n\t\t\t\t
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.019 | 0.004 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".