Bibliographic record
Abstract
Medicalisation is a key technology of power through which drug users are governed. It is the process by which non-medical problems come to be defined and treated as if they are medical issues. Another key strand of drug policy discourse in the UK, US and Canada operating alongside prohibition and punishment is that of public health. The technology of medicalisation underpins public health discourse, and compliments prohibition and punishment regimes. Medicalisation operates as a form of social control and regulation whereby social structural issues, such as poverty and social inequalities, are individualised and regarded as symptoms of a disease. It has provided legitimacy to punitive and intrusive policies and practices aimed at drug users. The interdependence of the criminal justice and treatment systems, and the way they reinforce each other in the governance of drug users, can be seen as a ‘deadly symbiosis’ (Wacquant, 2001). The technology of medicalisation is grounded in the disease model of addiction. Historically, this was dependent on a distinction between the normal and pathological, and involved a ‘stratification of the will’, whereby individuals with weak, defective characters were constructed as unable to act freely and responsibly. Constructions of a lack of will on the part of female users are bound up with notions of their mental health, sexuality and maternal role. They are situated as pathological, prone to addiction and weaker-willed than their male counterparts. In its more contemporary configurations, combined with discourses of ‘risk’, the disease model situates all drug users as rational, free, choice makers. Thus, female and male dependent users are constructed as, on the one hand, irresponsible, irrational, bad choice makers, and on the other, as responsible for their predicament and for coming off drugs. How female users navigate their way through disease and choice discourses and construct their identities is explored in this chapter. An overview of recent trends in drug treatment policies and practices, how female drug users are situated in relation to these, and the impact they have on their lives in the UK, US and Canada, is provided here. This includes a discussion of the ascendance of harm minimisation in relation to the HIV/AIDS pandemic, methadone maintenance, the current focus on ‘recovery’ and coerced treatment.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.227 | 0.075 |
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".