How should policymakers regulate the tetrahydrocannabinol content of cannabis products in a legal market?
Bibliographic record
Abstract
An increased use of high-potency cannabis products since cannabis legalization in the United States, Canada and elsewhere may increase cannabis-related harm. Policymakers have good reasons for regulating more potent cannabis in ways that minimize harm, using approaches similar to those used to regulate alcohol; namely, banning the sale of high-potency cannabis, setting a cap on tetrahydrocannabinol (THC) content and imposing higher rates of taxes on more potent cannabis products. Given the difficulty that US policymakers have had in regulating cannabis extracts and edibles, governments that are planning to legalize cannabis need to put policies on extracts into enabling legislation and evaluate the impact of these policies on cannabis use and cannabis-related harms.
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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 | 0.036 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 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".