A Post‐Pandemic Bail System: Lessons Learned From Supervising Accused During Covid‐19
Bibliographic record
Abstract
ABSTRACT Socio‐legal research has begun charting lessons learned from the pandemic; however, there is a noticeable gap regarding how the pre‐conviction phase of the criminal justice system was disrupted. Survey data from bail supervisors across Ontario, Canada, highlights which adaptations introduced during the pandemic are detrimental versus those that may be useful. Results suggest obstacles to accessing and navigating bail, a lack of rapport between accused and bail supervisors, and a dearth of social services, deepened pre‐existing deficits of the bail system and further eroded the regulatory and relational aspects of supervision. On the other hand, the benefits of hybrid reporting and flexibility in decision‐making allow us to reassess existing approaches to bail release. Our results reveal opportunities for improving the operation and legitimacy of bail supervision, while highlighting the tensions in risk management for those tasked with monitoring accused during the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".