Blame avoidance and the implementation of ambiguity in Canadian cannabis legalization
Bibliographic record
Abstract
Canadian cannabis legalization was destined to produce implementation gaps: it is a wicked policy issue, imbued with moral connotations, involving multiple levels of government, and formulated in an uncertain context. Those factors all pave the way for policy ambiguities. But why do these ambiguities appear? How do they shape the behavior of policy stakeholders? In this paper, we argue that, in order to manage the unique challenges of cannabis legalization, governments and central agencies rely on preemptive blame avoidance strategies. Ambiguities ensuing from the use of those strategies in turn enhance the responsibilities of implementers, while also limiting their capacity. We narrow down our analysis to the provinces of Ontario and Quebec, who have two contrasting regulatory models. Using data from semi-structured interviews and the grey literature, we highlight how ambiguities over the explicit and implicit objectives of legalization create opportunities to eschew blame. We find that blame avoidance structures policy resources and widens existing implementation gaps. Overall, this paper contributes to a better understanding of the role of blame avoidance in shaping implementation gaps.
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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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.022 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| 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".