Crafting effective regulatory policies for psychedelics: What can be learned from the case of cannabis?
Bibliographic record
Abstract
The turn of the century brought a resurgence of interest in psychedelics as a treatment for addiction and other psychiatric conditions, accompanied by extensive positive media attention and private equity investment. Government regulatory bodies in Australia, Israel, Canada and the United States now permit use of psychedelics for medical purposes. In the United States, citizen action and corporate financing have led to petitions and ballot initiatives to legalize psilocybin and other psychedelics for medical and recreational use. Given this momentum, policymakers must grapple with important questions that define whether and how psychedelics are made available to the public, as well as how companies produce and promote them. The current push to broaden the production, sale, and use of psychedelics bears many parallels to the movement to legalize cannabis in the United States and other nations-most notably, the use of poorly-evidenced therapeutic claims to create a de facto recreational market via the health care system. Experience with cannabis highlights the value of debating the question of legalization for nonmedical use as such rather than misrepresenting it as a medical issue. The lessons of cannabis policy also suggest a need to challenge hyping of psychedelic research findings; to promote rigorous clinical research on dosing and potency; to minimize the influence of for-profit industry in shaping policies to their economic advantage; and to coordinate federal, state, and local governments to regulate the manufacture, sale and distribution of psychedelic drugs (regardless of whether they are legalized for medical and/or recreational use).
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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 | 0.024 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.055 |
| Scholarly communication | 0.021 | 0.041 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.033 | 0.038 |
| Insufficient payload (model declined to judge) | 0.006 | 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".