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Record W2150033582

Pleasure, Freedom and Drugs: The Uses of ‘Pleasure’ in Liberal Governance of Drug and Alcohol Consumption

2009· article· en· W2150033582 on OpenAlexaff
Pat O’Malley, Mariana Valverde

Bibliographic record

VenueSSRN Electronic Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPleasureConsumption (sociology)Articulation (sociology)Corporate governanceGovernment (linguistics)Political scienceAestheticsSociologyPsychologyLawEconomicsSocial sciencePhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The paper explores the ways in which discourses of pleasure are deployed strategically in official commentaries on drug and alcohol consumption. Pleasure as a warrantable motive for, or descriptor of, drug and alcohol consumption appears to be silenced the more that consumption appears problematic for liberal government. Tracing examples of this from the 18th century to the present, it is argued that discourses of 'pleasure' are linked to discourses of reason and freedom, so that problematic drug consumption appears both without reason (for example 'bestial') and unfree (for example 'compulsive'), and thus not as 'pleasant'. In turn, changes in this articulation of pleasure, drugs and freedom can be linked with shifts in the major forms taken by liberal governance in the past two centuries, as these constitute freedom differently.

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)0.008
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0070.060
Scholarly communication0.0100.009
Open science0.0010.008
Research integrity0.0020.005
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 head0.014
GPT teacher head0.259
Teacher spread0.246 · 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 designTheoretical or conceptual
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

Citations16
Published2009
Admission routes1
Has abstractyes

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