'Staking a Claim': Legal and Illegal Cannabis Markets in Canada
Bibliographic record
Abstract
This study explores the emergence of the legal medical cannabis market in Canada and examines its impact on the wider medical cannabis market. The growing research investigating entrepreneurship and emerging markets have often failed to consider the identity narratives of the entrepreneurs across legal and illegal spaces and the importance of contextual influences, including wider social and political contexts. Drawing on a case study of cannabis entrepreneurs from illicit Medical Cannabis Dispensaries (MCDs) and legal Licensed Producers (LPs), I use 63 in-depth interviews, fieldwork, and primary and secondary sources, to provide a detailed account of the new industry’s emergence in 2014, which challenged an existing model of medical cannabis access. I explore the emergence of that market on a number of levels. In the first paper, I describe the rich history of medical cannabis access in Canada and the central role of MCDs in that process. By using the policy window framework to analyze two local-level responses to MCDs, I highlight the theoretical utility of using this approach to examine local-level drug policy initiatives and reform. The second paper investigates how MCDs have survived in Canada for two decades without legal or mainstream public support as “corestigmatized” organizations. By looking at the strategies MCDs employ to buffer stigma, share knowledge informally across organizations, and shelter themselves from police enforcement, I demonstrate how, compared to legal core-stigmatized organizations, MCDs must also navigate a host of legal risks because of their illicit status, which is tied to the source of their core stigma. In the third paper, I center on the experiences and narratives of the key players from both MCDs and LPs. I examine how these entrepreneurs understand and respond to the competitive landscape and draw on boundary work to claim jurisdiction over the medical cannabis market. Taken as a whole, I shift attention away from a moral assessment of the good itself (cannabis) and focus on the “practice of trade” (Anteby 2015: 631). I also strive to highlight the complex nature of medical cannabis access in Canada and how wider social, historical and political contexts matter to the landscape as it exists today. Finally, I bring the entrepreneurs’ experiences to the forefront. In particular, MCDs are often dismissed in larger debates because of their illicit status. Important policy implications for non-medical cannabis legalization and drug policy in Canada are also discussed, providing insight into the market and the cultural dynamics which could lead to successful reform and integration of long-standing players.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".