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Record W1859997102 · doi:10.1111/1467-9566.12342

On social plasticity: the transformative power of pharmaceuticals on health, nature and identity

2015· article· en· W1859997102 on OpenAlexafffund
Johanne Collin

Bibliographic record

VenueSociology of Health & Illness · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeuroethics, Human Enhancement, Biomedical Innovations
Canadian institutionsUniversité de Montréal
FundersEconomic and Social Research CouncilMedical Research CouncilCanadian Institutes of Health Research
KeywordsTransformative learningIdentity (music)Power (physics)SociologyPsychologySocial psychologyDevelopmental psychologyAestheticsPhilosophy

Abstract

fetched live from OpenAlex

This article proposes a theoretical framework on the role of pharmaceuticals in transforming perspectives and shaping contemporary subjectivities. It outlines the significant role drugs play in three fundamental processes of social transformation in Western societies: medicalisation, molecularisation and biosocialisation. Indeed, drugs can be envisaged as major devices of a pharmaceutical regime, which is more akin to the notion of dispositif, as used by Foucault, than to the sole result of high-level scheming by powerful economic interests, a notion which informs a significant share of the literature. Medications serve as a key vector of the transformation of perspective (or gaze) that characterises medicalisation, molecularisation and biosocialisation, by shifting our view on health, nature and identity from a categorical to a dimensional framework. Hence, central to this thesis is that the same underlying mechanism is at work. Indeed, in all three processes there is an evolving polarity between two antinomic categories, the positions of which are constantly being redefined by the various uses of drugs. Due to their concreteness, the fluidity of their use and the plasticity of the identities they authorise, drugs colonise all areas of contemporary social experiences, far beyond the medical sphere. A video abstract of this article can be found at: https://www.youtube.com/watch?v=djIBY7DHKW4&feature=youtu.be.

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)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.992
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0080.091
Scholarly communication0.0090.010
Open science0.0010.012
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.154
GPT teacher head0.468
Teacher spread0.314 · 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.

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

Citations51
Published2015
Admission routes2
Has abstractyes

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