Therapeutic jurisprudence revisited: The experience of criminal justice and treatment in Toronto's drug treatment court
Bibliographic record
Abstract
This thesis enlists therapeutic jurisprudence theory to evaluate programming at the Toronto Drug Treatment Court (TDTC). Through interviews with 17 TDTC professionals and observations of court proceedings, the study examines the roles and responsibilities of staff members, the relationship between the legal system and drug treatment initiatives, and the relevance of therapeutic jurisprudence to the operations of court personnel. The research shows support for the therapeutic jurisprudence perspective. According to the TDTC professionals interviewed, despite difficulties in reconciling the imperatives of treatment and control, both workers and participants benefited from the program's criminal justice-drug treatment partnership, and from the therapeutic aspects of the court process. A therapeutic jurisprudence analysis of drug treatment courts like the TDTC must take into account the justice-treatment relationship, the implications of this relationship for assessing the 'well-being' of 'drug-addicted' offenders, and the challenges of merging these two paradigms to address addictionmotivated crime.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.050 | 0.044 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".