Bibliographic record
Abstract
Could earmarking recreational marijuana taxes for investments in mental health offset the potential health consequences of marijuana legalization, while retaining its benefits to communities?, NYU News reported April 1. “If more and more states are passing recreational marijuana laws and adding excise taxes, then it would make sense that at least some of this is earmarked for mental health,” says NYU School of Global Public Health's Jonathan Purtle, an associate professor of public health policy and management and the author of a new JAMAHealth Forum paper arguing that the earmarked taxes have the potential to help millions. Right now, most states don't earmark marijuana tax revenue for mental health. Just six states — Connecticut, Illinois, Montana, New York, Oregon, and Washington — mention mental health in their recreational marijuana tax codes, but only in combination with substance use, providing no guarantee that any revenue will be spent on mental health in addition to substance use services. “We found that earmarking a quarter of marijuana tax revenue is not nominal — it's a lot of money, and could help a lot of people,” said Purtle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.011 | 0.017 |
| Insufficient payload (model declined to judge) | 0.244 | 0.098 |
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".