<i>Drug Policy and the Public Good</i>: a summary of the second edition
Bibliographic record
Abstract
Abstract The second edition of Drug Policy and the Public Good presents up‐to‐date evidence relating to the development of drug policy at local, national and international levels. The book explores both illicit drug use and non‐medical use of prescription medications from a public health perspective. The core of the book is a critical review of the scientific evidence in five areas of drug policy: (1) primary prevention programs in schools and other settings; (2) treatment interventions and harm reduction approaches; (3) attempts to control the supply of illicit drugs, including drug interdiction and law enforcement; (4) penal approaches, decriminalization and other alternatives; and (5) control of the legal market through prescription drug regimens. It also discusses the trend towards legalization of some psychoactive substances in some countries and the need for a new approach to drug policy that is evidence‐based, realistic and coordinated. The accumulated evidence provides important information about effective and ineffective policies. Shifting the emphasis towards a public health approach should reduce the extent of illicit drug use, prevent the escalation of new epidemics and avoid the unintended consequences arising from the marginalization of drug users through severe criminal penalties.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.029 | 0.010 |
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".