The socio-legal dynamics and implications of `diversion'
Bibliographic record
Abstract
This article explores the socio-legal dynamics and implications of the `John School' diversion programme for prostitution offenders in Toronto, Canada. The analysis is based on quantitative and qualitative data collected as part of an evaluation study of the programme conducted between 1999 and 2001. The analysis begins by exploring the socio-political forces that have shaped prostitution control in Canada over the last century and ultimately led to the emergence of the `John School' as a reform compromise. The article subsequently investigates the particular role of `victims' discourses within the rationale and practices of the `John School' initiative. It traces the ambiguous nature of the programme's objectives by contrasting its widely promoted `educational' and `constructive' aims with the more punitive qualities that emerge in practice. Drawing from the critical literature on informal justice and diversion, it is evidenced that the programme focuses disproportionately on participants from lower socio-economic classes. Serious questions are also raised with regards to `due process'. Particular attention is given to the requirement by participants to waive basic procedural rights in return for admission in the `John School' programme and the subsequent withdrawal of criminal charges. The degree of `choice' involved in accepting these conditions is evaluated with regard to the specific characteristics of the target population. Policy implications are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.036 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".