Where Is the Fairness in Canadian Cannabis Legalization? Lessons to be Learned from the American Experience
Bibliographic record
Abstract
Canada has received praise and international attention for its departure from strict cannabis prohibition and the introduction of a legal regulatory framework for adult use. In addition to the perceived public health and public safety benefits associated with legalization, reducing the burden placed on the individuals criminalized for cannabis use served as an impetus for change. In comparison to many jurisdictions in the United States, however, Canadian legalization efforts have done less to address the harms that drug law enforcement has inflicted on individuals and communities. This article documents the racialized nature of drug prohibition in Canada and the US and compares the stated aims of legalization in in both jurisdictions. The article outlines the various reparative measures being proposed and implemented in America and contrasts those with the situation in Canada, arguing, furthermore that the absence of social justice measures in Canadian legalization is an extension of the systemic racism perpetuated under prohibition.
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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.049 | 0.039 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".