The Effects of an Accused’s Previous Experience in the Canadian Bail System
Bibliographic record
Abstract
The Canadian bail system is complex and multifaceted, encompassing remand incarceration, bail court, and bail release into the community. Accused who are charged with a criminal offence, and must endure the bail process, are subject to experiencing punishment without a finding of guilt. Utilizing qualitative interview data, I argue that throughout the bail process, an accused person’s previous experience within the criminal justice system works to mitigate the hardships caused by the bail and remand system. An accused person’s previous experience contributes to their knowledge and understanding, their stress and uncertainty, and alters the relationships they have with criminal justice actors during the bail process. Previous experience contributes to an increase in the familiarity and power that an accused person possesses while navigating the bail process. This power impacts an accused person’s relative bail and remand experience and decreases the state’s ability to adequately control people who are more experienced with the bail system. This research points to the need for further research, as well as alterations to the current Canadian bail and remand system.
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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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".