Doctor or drug dealer? International legal provisions for the legitimate handling of drugs of abuse
Bibliographic record
Abstract
In this article, we compare how five jurisdictions (the USA; UK; Canada; New Zealand; and Australia) balance the disparate objectives of preventing the misuse of drugs and allowing their legal use for medical purposes. The statutory law underpinning each country’s method of categorising drugs depicts distinctive outlooks from the different jurisdictions, as each works towards these same goals. In examining how each country’s legislation deals with controlled substances, initial consideration will be given to whether drugs are categorised once only, or twice: once for dealing with their criminal misuse; and again for ensuring their safe medicinal use. In effectively dealing with criminal activities associated with drugs of abuse, Australia’s system of imposing penalties based on the quantity of a drug possessed, rather than on its grouping with other drugs of a broadly similar type offers the most flexibility. In terms of managing the legitimate use of such drugs, however, it is perhaps the least flexible of the four jurisdictions operating parallel systems of categorisation. The greatest level of flexibility is offered by Canada and the UK, which have functionally very similar protocols in this respect.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".