‘It’s a set up’: examining the relationship between bail conditions and the revolving door of justice
Bibliographic record
Abstract
Research has consistently drawn attention to how pre-trial release with bail conditions has the unintended consequence of setting accused up to accumulate further criminal charges. In response, legislative reforms to increase the courts’ knowledge of the barriers accused face in complying with bail conditions have ensued. In light of these reforms, we assess whether and how bail conditions are still setting accused up to fail, using data from in-depth interviews with bail supervisors in Ontario, Canada. Given their involvement with the courts, accused, police and social service agencies, this sample provides a unique lens to observe how pre-trial conditions contribute to the revolving door of the criminal justice system. Our findings reveal evidence of a continuing trend whereby courts assign conditions with which accused have little realistic chance of complying; both systemic barriers and more mundane errors continue to undermine accused chances of adhering to conditions of bail. Nevertheless, some shifts in the types of conditions assigned are evident, notably regarding more tailored abstinence and treatment conditions. Importantly, however, bail supervisors caution that releasing accused with fewer conditions alone is insufficient to set accused up for success.
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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 | 0.008 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.017 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".