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Record W622923335 · doi:10.1017/cbo9781139162142

The Drug Effect

2011· book· en· W622923335 on OpenAlexaboutno aff
Suzanne Fraser, Robyn Dwyer, Kane Race, Susan Boyd, David Moore, Helen Keane, Nancy Campbell, kylie valentine, Toby Seddon, Craig Reinarman, Ian Warren, Karen Duke, Desmond Manderson

Bibliographic record

VenueCambridge University Press eBooks · 2011
Typebook
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsAgency (philosophy)SociologyMateriality (auditing)SubjectivityAddictionField (mathematics)Social workPublic relationsCriminologyPolitical scienceEngineering ethicsSocial scienceLawMedicineEngineeringEpistemology

Abstract

fetched live from OpenAlex

The Drug Effect: Health, Crime and Society offers new perspectives on critical debates in the field of alcohol and other drug use. Drawing together work by respected scholars in Australia, the US, the UK and Canada, it explores social and cultural meanings of drug use and analyses law enforcement and public health frameworks and objectives related to drug policy and service provision. In doing so, it addresses key questions of drug use and addiction through interdisciplinary, predominantly sociological and criminological, perspectives, mapping and building on recent conceptual and empirical advances in the field. These include questions of materiality and agency, the social constitution of disease and neo-liberal subjectivity and responsibility. This book provides a fresh scholarly perspective on drug use and addiction by collecting top quality original work, written by a mix of international leaders in the field and emerging scholars working at the cutting edge of research.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0380.008

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 head0.017
GPT teacher head0.209
Teacher spread0.192 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations102
Published2011
Admission routes1
Has abstractyes

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