Simultaneously treatable and punishable: Implications of the production of addicted subjects in a drug treatment court
Bibliographic record
Abstract
Drug treatment courts (DTCs) in Canada are often framed as a progressive approach to managing people with addictions who come into contact with the law. In the specialized courts, participants are considered to have a health issue (addiction) that is best managed through a judicial system. Thus, participants in DTCs are considered to be a different type of subject than those in the traditional judicial system. Using data from a 25-month critical ethnography in the Ottawa DTC, this article explores how participants in DTCs are constructed as addicted subjects. Key characteristics of the addicted subject in the Ottawa DTC are presented: (1) an individualistic universal subject who is genderless and dislocated from context; and (2) a treatable subject who requires therapeutic interventions and who displays gratitude and a positive attitude. Implications of these characteristics are explored including (1) how a universal subject affects the type of treatment services offered, particularly gender-specific treatment; (2) how DTCs set up a system whereby individuals who are criminalized gain priority access drug treatment and other services; and (3) how participants receive criminal punishments for non-criminal behavior. It is argued that there is a simultaneous construction of the addicted subject as both treatable and punishable, which has serious consequences on participants in DTCs.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.069 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".