Drug Policy and the Public Good
Bibliographic record
Abstract
Abstract Drug use represents a significant burden to public health, through disease, disability and social problems, and policy makers are becoming increasingly interested in how to develop evidence-based drug policy. It is therefore crucial to strengthen the links between addiction science and drug policy. Drug Policy and the Public Good is collaboratively written by an international group of career scientists, to provide an analytical basis on which to build relevant global drug policies, and to inform policy makers who have direct responsibility for public health and social welfare. Drug Policy and the Public Good presents the accumulated scientific knowledge on illicit drugs that has direct relevance to the development of drug policy on local, national, and international levels. The authors describe the conceptual basis for a rational drug policy, and present new epidemiological data on the global dimensions of drug misuse. The core of the book is a critical review of the cumulative scientific evidence in five general areas of drug policy: primary prevention programs in schools and other settings; supply reduction approaches, including drug interdiction and legal enforcement; treatment interventions and harm reduction approaches; criminal sanctions and decriminalization; and control of the legal market through prescription drug regimes. The final chapters discuss the current state of drug policy in different parts of the world, and describe the need for a new approach to drug policy that is evidence-based, realistic, and coordinated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".