Blowing in the Wind: Cannabis Legalization, Insiders, and Methodological Insights from British Columbia
Bibliographic record
Abstract
As jurisdictions worldwide wrestle with the costly consequences of criminalization, some are extending calls by the United Nations (U.N.) to promote alternatives to "conviction and punishment" by embracing cannabis legalization and regulation.By 2023, Germany and Malta will move toward legalizing and regulating the sale of cannabis, following the lead of Uruguay and Canada.While the adverse impacts of cannabis prohibition on the criminal justice system have long been noted (Kaplan, 1970), evidence of the benefits of legalization is beginning to emerge.Between 1998 and 2018, police arrested between 34 ABSTRACT Legalizing cannabis in Canada has proven momentous in some ways and insufficient in others.This paper presents findings from a re-analysis of two studies on cannabis legalization conducted in British Columbia (B.C.) before and after legalization.Prioritizing public health over access appears to prolong stigmatization, complicate policing, and undermine efforts to disrupt illicit cannabis markets.We outline three contributions to the nascent postprohibition cannabis research agenda.First, we demonstrate the potential for secondary data analysis (SDA) and model an approach to address recent concerns about this practice.Second, we show the value of insiders when assessing cannabis policy by demonstrating support for previous findings while extending and complicating others.Third, and finally, we identify four themes from the data to guide the future study of cannabis within criminology.These include the impact of public education on cannabis stigma, post-legalization policing changes, the dangers of over-regulation, and the effects of legalization on crime.In addition, we consider the role of race, ethnicity, and injustice, which while largely absent in this study, remains an essential issue in cannabis policy.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".