CARE AND CONTROL?: EXAMINING THE ROLE OF THE PENAL VOLUNTARY SECTOR IN BAIL SUPERVISION
Bibliographic record
Abstract
This dissertation examines a Bail Supervision Program (BSP) operated by a non-governmental organization in Ontario, Canada. The data includes over 200 hours of informal observations and conversations at a BSP in Ontario, Canada, coded client case files that closed in 2019 (n= 212), interviews with accused (n= 20), and interviews with bail supervisors (n= 9). This dissertation follows the three-manuscript option and presents three main arguments. The first manuscript argues BSP is a tool used by courts to intervene in the lives of marginalized accused without first needing to secure a conviction. The BSP monitors, tests, and sorts accused, assessing capacity for self-governance and docility through procedural hassle, then relaying this information to the court to be used to determine an appropriate case outcome. The second manuscript argues the BSP provides a critical support role for accused: avoiding consequences of pre-trial detention, offering guidance in navigating the criminal legal process, acting as a resource and information hub, and offering emotional support for accused during a stressful period. While the supporting role is beneficial for accused and the court, accessing support is contingent on accepting coercive conditions that may further entrench accused in the criminal justice system. The third manuscript outlines concerns with the supervisory role of the BSP finding the program does not impact compliance with conditions and is not well positioned to detect condition violations outside of program specific conditions. Instead, the onerous BSP conditions (i.e., weekly reporting to the BSP and program referrals) contribute to a cycle of criminalization through increasing the risk of incurring additional charges for failing to report as required. This research provides needed knowledge of the utility of BSPs from the perspectives of supervised accused people and bail supervisors.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".