Smoke-free spaces over time: a policy diffusion study of bylaw development in Alberta and Ontario, Canada
Bibliographic record
Abstract
Policy diffusion is a process whereby political bodies 'learn' policy solutions to public health problems by imitating policy from similar jurisdictions. This suggests that diffusion is a critical element in the policy development process, and that its role must be recognised in any examination of policy development. Yet, to date, no systematic work on the diffusion of smoke-free spaces bylaws has been reported. We examined the diffusion of municipal smoke-free bylaws over a 30-year period in the provinces of Alberta and Ontario, Canada, to begin to address this gap and to determine whether spatial patterns could be identified to help explain the nature of policy development. Bylaw adoption and change were analysed within local, regional, and provincial contexts. Geographical models of hierarchical and expansion diffusion in conjunction with the diffusion of innovations framework conceptually guided the analyses. Study findings contribute to a broader understanding of how and why health policies diffuse across time and place. Policy development can be a powerful mechanism for creating environments that support healthy decisions; hence, an understanding of policy diffusion is critical for those interested in policy interventions aimed at improving population health in any jurisdiction.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".