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Record W2962637550 · doi:10.1111/add.14734

<i>Drug Policy and the Public Good</i>: a summary of the second edition

2019· article· en· W2962637550 on OpenAlexaff
Thomas F. Babor, Jonathan P. Caulkins, Benedikt Fischer, David Foxcroft, María Elena Medina‐Mora, Isidore Obot, Jürgen Rehm, Peter Reuter, Robin Room, Ingeborg Rossow, John Strang

Bibliographic record

VenueAddiction · 2019
Typearticle
Languageen
FieldMedicine
TopicHIV, Drug Use, Sexual Risk
Canadian institutionsCentre for Addiction and Mental HealthUniversity of Toronto
Fundersnot available
KeywordsDecriminalizationLegalizationHarm reductionDrug controlPublic healthInterdictionUnintended consequencesIllicit drugMedical prescriptionDrugHarmPsychological interventionLaw enforcementScientific evidenceDeterrence theoryMedicinePublic policyPublic economicsPolitical sciencePsychiatryPharmacologyLawEconomics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.255
Threshold uncertainty score0.319

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.273
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2019
Admission routes1
Has abstractyes

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