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Record W4297850203 · doi:10.51952/9781847426734.bm001

Research methods

2015· book-chapter· en· W4297850203 on OpenAlexaboutno aff
Natasha Du Rose

Bibliographic record

VenuePolicy Press eBooks · 2015
Typebook-chapter
Languageen
FieldHealth Professions
TopicCommunity Health and Development
Canadian institutionsnot available
Fundersnot available
KeywordsEmpowermentNarrativePsychological interventionEquity (law)Corporate governanceDrugPolitical scienceSociologyCriminologyPsychologyBusinessLawPsychiatry

Abstract

fetched live from OpenAlex

This book is about the ways in which the governance of illicit drug use shapes female dependent drug users’ lives. It argues female drug users’ subjectivities, and hence their experiences, are shaped and regulated by drug policies. The relationship between the social regulation of female drug users and the “making up” of their identities is investigated. It explores the dominant governmental technologies of power from which the key constructions of women as “problematic” drug users emanate in the UK, Canada and the US: punishment and prohibition, medicalisation and welfarisation. It also investigates the meanings that women who identify as having dependent drug use attach to their drug use and themselves. Insights are gathered from the in-depth accounts of 40 female drug users in the UK. The book argues, in the regulation of illicit drug using women, particular subjectivities are constructed which, in themselves, become part of the narrative sustaining women in their problematic drug use. It asserts that female users experience drug policy as something that exacerbates their social and economic marginalisation and contributes to their lives being plunged into further marginalisation. At the same time, it analyses the contradictory choices, adaptations and resistances of female users. Although women users internalise many of the negative constructions of them found in policy discourse they also find ways to resist them. Popular misconceptions of female users which condition oppressive interventions are subverted with the hope of contributing to the formulation of drug policies based on empowerment, gender equity and social justice.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.522
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.006
Insufficient payload (model declined to judge)0.0010.001

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 head0.796
GPT teacher head0.702
Teacher spread0.094 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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