Drug Treatment Courts According to Criminal Defence Lawyers
Bibliographic record
Abstract
Abstract Defence lawyers working in lower criminal courts are increasingly invited to consider a variety of holistic or alternative strategies like drug treatment courts (DTC). This raises new ethical and practical questions. Scholars have been critical, showing how specialized courts circumvent the principle of the presumption of innocence, impose onerous conditions and surveillance, and lack the resources required to support participants over the long term. What is not known, however, is how defence lawyers representing marginalized clients talk about and engage with DTC programs. Our paper examines this, drawing from interviews with defence counsel working in Toronto and Montreal (n=98). We describe and discuss when and why participants report being either more supportive or more critical of drug treatment courts, and how they borrow from therapeutic justice in their “regular” practice. Our discussion engages with questions about access to health and social support resources, about interdisciplinary interventions and the ways in which people are criminalized rather than helped.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.014 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".