Sentenced to treatment/sentenced to harm: Women, risk and the drug treatment courts
Bibliographic record
Abstract
Rehabilitative endeavours that wed psy knowledges and legal sanctions promise to lower the risk an offender poses of recidivism by offering change oriented practices designed to render her less criminogenic. Such legally ensconced therapeutic practices are often packaged in the language of risk management. Under this rubric, treating particular pathologies is not only of benefit to the individual but also is intended to lessen the potential harm an individual might inflict on the public. These governing schemes appeal to contemporary neoliberal regimes as they promise objectivity and serve to responsibilize the individual, eschewing social and structural understandings of criminality. At the same time, the advent of risk management and psybased programming are touted as humanizing forces designed to counter the alleged ‘punitive turn’ by drawing on welfarist practices intended to rehabilitate (Meyer and O’Malley 2004; Moore and Hannah-Moffat 2004). The intersection of risk, law and psy is not, contrary to the prevailing discourse, inherently benevolent and progressive. Working to reduce the risk of harm to the public paradoxically involves the constitution of particular risks of harm to the individual caught up in such schemes. That is, initiatives designed to both treat AND punish through the same acts rely on placing people in conflict with the law in risky situations. This constitution of risks is particularly acute for women. Women’s statuses as both women and mothers (would be and actual) form the bases of particular, gendered harms. In this chapter we explore the experiences of women enrolled in Canadian drug treatment courts (DTC). We discover that, in facing the harms constituted for them in risk management schemes, women deploy their own forms of counter risk management. Responding to gendered risks, these practices are themselves gendered and designed to assist women in navigating their criminal justice involvement.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.022 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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.
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".