Reasonable Bail or Bail at All Costs? Exploring the Role of Defence Lawyers in Bail Preparations and Negotiations
Bibliographic record
Abstract
Accused persons have a constitutional right to reasonable bail, but they regularly face delays to release and excessive bail conditions. Bail decisions are largely shaped by private, out-of-court negotiations between Crown attorneys and defence lawyers. The accused relies on the professional expertise of defence lawyers to navigate bail negotiations and get them the best outcome possible. However, research has not yet explored how defence lawyers prepare for and negotiate bail. The current study utilizes data from 18 semi-structured interviews with Ontario-based defence lawyers. The findings show that while defence lawyers care about and argue for reasonable bail terms, their ultimate goal is to obtain a release for their client, regardless of whether the terms of release are the least restrictive according to relevant legal standards. Defence lawyers also place high importance on collecting and strategically using information about the case and the Crowns and justices they work with. Additionally, they often behave in a risk-averse and cooperative manner because disagreeing with Crown requests and arguing matters before a justice risks delaying or destroying the client’s release. Crown and justice discretionary power over the defence means that policy solutions that seek to protect accused persons’ right to reasonable bail must focus on altering Crown and justice behaviour.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".