Making "Medical": How Psychedelics Are Becoming Legal in Canada
Bibliographic record
Abstract
As legal restrictions loosen, psychedelic-assisted therapies are advancing at an unprecedented pace and scope in Canada and the US. Presented as a miracle cure for post-traumatic stress, depression, and other psychological disorders, psychedelics are being touted to treat post-pandemic mental health crises. In this paper, drawing on Science and Technology Studies, I ethnographically trace the ongoing process and practices involved in transforming illegal psychedelics into a regulated medicine in Canada, paying particular attention to regulatory pathways and the development of networks involved in psychedelic advocacy. Using these pathways as a methodological “sampling device,” I map the main actors, their mutual relationships, and the resources mobilized to advocate the regulatory goals. By tracing this regulatory networking, I also demonstrate how domination and professionalization are being enacted alongside regulatory advocacy, raising questions about future equitable access to psychedelic-assisted therapies.\nAlors que les restrictions légales s’assouplissent, les thérapies assistées par les psychédéliques progressent à un rythme et avec une ampleur sans précédent au Canada et aux États-Unis. Présentés comme un remède miracle contre le stress post-traumatique, la dépression et d’autres troubles psychologiques, les psychédéliques sont vantés pour traiter les crises de santé mentale postpandémiques. Dans cet article, qui s’inspire des études sur la science et la technologie, je retrace de manière ethnographique le processus en cours et les pratiques impliquées dans la transformation des psychédéliques illégaux en médicaments réglementés au Canada, en accordant une attention particulière aux voies réglementaires et au développement des réseaux impliqués dans la défense des intérêts des psychédéliques. En utilisant ces voies comme un « dispositif d’échantillonnage » méthodologique, je cartographie les principaux acteurs, leurs relations mutuelles et les ressources mobilisées pour défendre les objectifs réglementaires. En retraçant ce réseau réglementaire, je démontre également comment la domination et la professionnalisation sont mises en œuvre parallèlement à la défense de la réglementation, ce qui soulève des questions quant à l’accès équitable futur aux thérapies assistées par les psychédéliques.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.042 | 0.019 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".