Introduction to the Special Issue on Cannabis Legalization: Cannabis Policy at the Twilight of Prohibition
Bibliographic record
Abstract
The title of this special issue is borrowed from our colleague Matt Reid, who catalogued the range of cannabis stigmas that persisted even as prohibition appeared to be receding (Reid, 2020).Indeed, cannabis policy has changed rapidly over the past decade.This not only has created opportunities for meaningful reform but also led to a variety of new problems and challenges.This special issue brings together articles from various international contexts to address some of these emerging issues.It is difficult to observe the march of cannabis liberalization across the globe without concluding that cannabis prohibition is unraveling.Long-time opponents of reform have joined the boards of cannabis companies, cannabis advertising adorns billboards and social media sites, and cannabis is increasingly seen as a commercial good rather than a dire existential threat to civilization.Because of this perceptual shift, one might assume that cannabis-related issues will become less relevant to social scientists and legal scholars.However, there are indications that suggest this is not the case.From countries that prohibit cannabis use to those where legalization is decades old, a series of ironies, unintended consequences, and adverse outcomes continue to plague cannabis policy.One issue is that contemporary cannabis research often emphasizes Northern jurisdictions where liberalization dominates the policy debate.However, Western and especially U.S.-inspired models of drug control have led to considerable harm in the global South.These harms persist.For example, in this issue, Nelson and Klantschnig examine illicit cannabis in Nigeria based on interviews and long-term interactions with illicit cannabis cultivators and traders in both rural and urban contexts.They explore the socioeconomic benefits that cannabis offers illegal market actors, document the threats they face from repressive law enforcement, and report concerns about a future legal cannabis market fraught with inequities.African countries are not the only supporters of the prohibitionist approach on the international level, of course.Of continued interest is how alarmist messaging about the risks of cannabis continues to proliferate.Political rhetoric around cannabis has historically produced ironic results.This is especially true when claims being made are not based on empirical research and instead stem from efforts to scare young people away from drug use.This has a long history in North America and is perfectly illustrated in the infamous "This is your brain on drugs" commercial featuring an egg being fried, symbolizing any and all illicit drug use. 1 A similar pattern emerges in a paper by Bebre, published in this issue.Based on her analysis of interviews with people in Latvia who use cannabis, when state actors embellish the dangers of cannabis use, people often interpret it as a sign of a lack of knowledge of the subject matter and proceed to ignore their claims.State actors are advised to use empirical evidence and realistic
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.060 | 0.027 |
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".