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Record W2091367248 · doi:10.1177/0020715213484482

The criminal law and public health context of drug policy support among young people in the European Union

2013· article· en· W2091367248 on OpenAlexvenueno aff
Mike Vuolo

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPolicy Transfer and Learning
Canadian institutionsnot available
FundersEuropean Commission
KeywordsCriminalizationEurobarometerDecriminalizationEuropean unionContext (archaeology)Harm reductionSocial policyPublic policyPublic healthPolitical scienceLawBusinessMedicine

Abstract

fetched live from OpenAlex

This article addresses whether state-level policy differences in the European Union (EU) are associated with young people’s support for policies toward drug users, namely criminalization of drug use and availability of syringe exchanges. While historical institutionalism provides reason to expect such an association, more proximal individual-level influences, as well as the EU supranational agenda encouraging a unified approach to drug policy, may render the state-level unimportant. Using multilevel modeling of a 2002 Eurobarometer cross-national survey of young people, the results show a close alignment of individual-level support and state-level policies of decriminalization and harm reduction, net of individual characteristics. The findings suggest that any EU-level effort to unify countries’ policies concerning drug users will encounter difficulty because attitudes – even among young people – are aligned with and supportive of existing state policies.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.003
Scholarly communication0.0040.002
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.068
GPT teacher head0.399
Teacher spread0.332 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2013
Admission routes1
Has abstractyes

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