Bibliographic record
Abstract
Compassion Clubs began providing storefront access to medical cannabis in Canada in 1997, shortly before Canada formally recognized cannabis as having medical benefits in 2001. Medical and non-medical cannabis dispensaries proliferated before cannabis was legalized in 2018. Compassion Clubs bear remarkable similarities to the Cannabis Social Clubs (CSCs) found throughout Europe and South America. By creating opportunities to access cannabis under existing constitutional pathways, innovating in the absence of formal regulations, utilizing a non-profit approach, and creating relationships with consumers and cultivators, the two models have demonstrated the successful functioning of a non-commercial approach to cannabis. Although developed in criminalized contexts, these models have much to offer in a legalized environment. While Canada’s legalization of cannabis was a victory for advocates of cannabis reform, the relationships and ethos of the Compassion Clubs and cannabis dispensaries were abandoned in favour of a profit-seeking system that alienated consumers, retailers, and cultivators from each other. In this chapter we highlight the history of Compassion Clubs and cannabis dispensaries, compare Compassion Clubs and CSCs, and identify challenges faced in transitioning to a regulated recreational market. We propose aspects of non-commercial cannabis distribution that could inform the ever-evolving cannabis policy discussion in Canada and beyond.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.022 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".