Turning failure into success: what does the case of Western Australia tell us about Canadian cannabis policy-making?
Bibliographic record
Abstract
Cannabis policy in Canada is a puzzling affair. Since the 1960s and as recently as 2006, several policy windows have opened promising evidence-based cannabis law reform only to be slammed shut before achieving meaningful change. This ‘saga of promise, hesitation, and retreat’ has motivated Canadian cannabis researchers to investigate the reasons behind this policy inertia. These single-jurisdiction analyses have resulted in interesting yet necessarily tenuous findings. Fischer's (1999) Policy Studies article suggests the need for an analysis of Canadian cannabis policy in comparative context and offers Australia as a point of departure. This article addresses this analytic task by examining two recent case studies in cannabis policy. Specifically, borrowing Kingdon's (1995) concept of a policy window, it contrasts Canada's failure to decriminalise minor cannabis offences between 2001 and 2006 with Western Australia's successful decriminalisation of cannabis possession and production for personal use between 2001 and 2004. In particular, it appears that a lack of support from law enforcement and cannabis users, conflicting evidence and risk associated with a lack of an evaluation plan all combined with a weakened electoral mandate for the government to contribute to a perception that cannabis decriminalisation was not politically feasible. Additional variables worthy of further inquiry are also discussed.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.035 | 0.022 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".