Opening windows and closing gaps: a case analysis of Canada’s 2009 tobacco additives ban and its policy lessons
Bibliographic record
Abstract
Abstract Background In 2009, Canada adopted legislation (Bill C-32) restricting the sale of flavoured tobacco products, one of the first in the world. This study examines the agenda-setting process leading to the adoption of Bill C-32. Methods This research was conducted using a case study design informed by Kingdon’s Multiple Streams framework and Heclo’s policy learning approach. In-depth interviews were conducted with key informants from government, health-based non-governmental organizations (NGOs), trade associations and the cigar manufacturing sector (n = 11). Public documents produced by media (n = 19), government (n = 11), NGOs (n = 15), as well as technical reports (n = 8) and formal stakeholder submissions (n = 137) were included for analysis. Data were coded with the objective of understanding key events or moments in the lead up to the adoption of Bill C-32 and the actors and arguments in support of and opposition to Bill C-32. Results The findings point to the importance of a small but active group of NGOs who worked to publicize the issue and eventually take advantage of an open policy window. Our analysis also illustrates that even though consensus was developed about the policy problem and civil society was able to garner political support to address the problem, disagreement and dissent pertaining to the technical dimensions of the policy solution created loopholes for the tobacco industry to exploit. Conclusions NGOs remain a critical factor in efforts to strengthen tobacco control policy. These organizations were able to mobilize support for the tobacco flavouring ban adopted at the Federal level in Canada, and although the initial Bill had major limitations to achieving the health objectives, the persistence of these NGOs resulted in amendments to close these loopholes.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".