Toward a “Post-Legalization” Criminology for Cannabis: A Brief Review and Suggested Agenda for Research Priorities
Bibliographic record
Abstract
Cannabis control policies in a few countries have recently shifted from criminal prohibition-based regimes to legalization of use and supply. While cannabis’ newly emerging status of legality may suggest a coming “end” for criminology-based interest in the drug, these fundamental changes rather open a window to a new set of criminological research issues and questions, mostly focusing on cannabis use and related behaviors, and their relation to crime and justice. Based on a joint, personal record of several decades of criminological research on cannabis, we briefly review the rationale for five fundamental topics and issues of cannabis-related research associated with legalization. These include: 1) the deterrent effect of prohibition; 2) illicit production, markets and supply in a legalization regime; 3) use enforcement; 4) cannabis-impaired driving; 5) cannabis and crime. This constitutes an—albeit subjectively selective—“post-legalization” research agenda for a cannabis-focused criminology. Other possible areas of research focus or interest within fundamentally different paradigms of criminology (e.g., “critical criminology”) are identified and encouraged for development. Overall, the proposed research agenda for a post-legalization cannabis criminology should both contribute discipline-specific knowledge to improved cannabis-related public health and safety as well as allow for important debate and development in this evolving and important research field while entering a new (“post-legalization”) era.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".