Cannabis, Moral Entrepreneurship, and Stigma: Conflicting Narratives on the 26 May 2016 Toronto Police Raid on Cannabis Shops
Bibliographic record
Abstract
On May 26, 2016, the police raided 43 cannabis dispensaries in Toronto, Canada, making 90 arrests. This article aims to describe the narrative of the responsible state agencies concerning the police raid and compare it to the narrative of those who opposed it, such as activists, as well as consumers and sellers of cannabis. While such concepts as moral entrepreneur, moral panic, and moral crusade have traditionally been used to study those in power, I will employ them to explore both the state narrative and ways in which counterclaims-makers resisted it. In order to do so, I will further develop the concept of moral entrepreneurship and its characteristics by relating it to studies of moral panics and social problems. This article will be guided by the following question: How did each party socially construct its cannabis narrative, and in what way can we use the concept of moral entrepreneurship to describe and analyze these narratives as social constructions? I have investigated the media coverage of the raid and ethnographically studied shops in Toronto in order to study the narratives. My findings show that both parties used a factual neutral style, as well as a dramatizing style. The later includes such typical crusading strategies as constructing victims and villains and presenting the image of a dystopian social world. In order to explain the use of these strategies, we will relate them to the shifting wider social and historical context and to the symbolic connotation of cannabis shops in Toronto in particular and in Canada as a whole.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".