Measuring Policy Diffusion in Federal Systems: The Case of Legalizing Cannabis in Canada under Time Constraints
Bibliographic record
Abstract
Abstract Existing studies of policy diffusion rely on quantitative or qualitative methods depending on the number of cases and the policy at hand. Studies of diffusion in Canada, for instance, almost exclusively use qualitative techniques due to the limited number of subnational units. In this article, we explore whether machine learning techniques can complement qualitative approaches in these contexts. In 2015, the Canadian federal government decided to impose the legalization of cannabis and gave the provinces and territories a short time frame to develop and implement legislation. Previous qualitative research on this case found that within-province policy development was more salient than interprovincial diffusion. Using a plagiarism detection software, we find limited evidence of exact matches between provincial legislation, but a cosine score approach reveals significant similarities across provinces. These results suggest that computational and qualitative techniques together should be used where possible to identify and analyze policy diffusion in certain contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".