Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.233 | 0.106 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".