A BILL TO PROVIDE FOR THE MEDICAL USE OF MARIJUANA, BEFORE THE HOUSE COMMITTEE ON HEALTH, HUMAN SEVICES, AND ELDERLY AFFAIRS
Bibliographic record
Abstract
I applaud the members of the House Committee on Health, Human Services, and Elderly Affairs for holding this hearing regarding House Bill 774, which seeks to shield qualified patients who use cannabis therapeutically with a doctorʹs recommendation from criminal prosecution. The physician-supervised use of medicinal cannabis is a scientific and public health issue. It should not be held hostage by the so-called “war on drugs ” or by broader public policy disputes regarding the legalization of marijuana or other controlled substances for recreational purposes. Professionally, I have examined the science surrounding the medicinal use of cannabis and its active compounds (known as cannabinoids) since 1995, publishing more than 100 articles and white papers on the subject as the senior policy analyst for NORML (the National Organization for the Reform of Marijuana Laws) and the NORML Foundation. I also have worked as a consultant for London’s biotechnology firm GW Pharmaceuticals (www.gwpharm.com) – the only company legally licensed in the world to cultivate medical cannabis and perform clinical trials on various preparations of oral spray cannabis extracts. These extracts are legally available by prescription in Canada as well as on a limited basis in Spain and the United Kingdom under the trade name Sativex. Most recently, I researched, edited, and authored the booklet, “Emerging Clinical Applications for
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.076 | 0.053 |
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".