Using the 3I+E Framework to assess provincial policy decisions for the sale of cannabis in Ontario, Saskatchewan and Quebec
Bibliographic record
Abstract
Objective: This paper examines policy decisions regarding public or private retail models chosen for the recreational use of cannabis in the provinces of Ontario, Saskatchewan and Quebec to demonstrate the application of the 3I+E framework for policy analysis. Methods: The 3I+E framework includes considerations of institutions, interests, ideas and external factors that play a role in adopting a particular policy. A retrospective comparative approach using this framework was conducted. Relevant newspaper articles, press releases, consultation reports and primary policy papers were reviewed. Results: Ontario employed a mixed model for the sale of cannabis while Saskatchewan chose to fully privatize cannabis retail within the province and Quebec decided to sell through the public sector. Government institutions, particularly the party in power and the number of seats they hold, as well as existing policy legacies for alcohol retail, appeared to have a strong ability to influence policy decisions in all three jurisdictions. Interest groups, including municipal and labor unions and private cannabis companies had a limited role in swaying government decisions toward a particular model. Beliefs and values of citizens regarding cannabis retail did not appear to play a large role. In Ontario particularly, an external factor, namely a major political shift towards a conservative government had a large role in the mixed model chosen in the jurisdiction. Conclusion: Overall, the policy decision for cannabis retail is multifactorial and the interaction between stakeholders and interest groups with the government influences which model was ultimately chosen in each jurisdiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".