Police-Generated Evidence in Bail Hearings: Generating Criminality and Mass Pretrial Incarceration in Canada
Bibliographic record
Abstract
Systemic racism in policing impacts many aspects of the criminal legal system including the system of judicial interim release. This paper traces the ways in which reliance on police-created evidence at bail hearings might contribute to mass pretrial incarceration in Canada which is disproportionately felt by Indigenous, Black, and marginalized people. The police synopsis and police-created criminal records are state knowledge created for state purposes. This state-created evidence in fact generates race and racialization; all of the structural inequalities built into the system of policing become relied on at bail hearings through police-created evidence which contributes to mass pretrial incarceration in Canada. In this way, policing contributes to the creation of “criminality” and it is Indigenous, Black and “vulnerable” people who disproportionately become criminalized and contained in jails. The paper concludes by pulling together the ways in which police-generated evidence constructs criminality and exemplifies how heavily weighted bail hearings are in favour of the state.\nLe racisme systémique dans le maintien de l’ordre a des répercussions sur de nombreux aspects du système juridique pénal, notamment sur le système de mise en liberté provisoire par voie judiciaire. Cet article montre comment le recours aux preuves créées par la police lors des audiences de libération sous caution peut contribuer à l’incarcération massive avant le procès au Canada, qui touche de manière disproportionnée les Autochtones, les Noirs et les personnes marginalisées. Le synopsis de la police et les casiers judiciaires créés par la police sont des connaissances étatiques créées à des fins étatiques. Ces preuves créées par l’État génèrent en fait le racisme et la racialisation; toutes les inégalités structurelles intégrées au système de maintien de l’ordre sont invoquées lors des enquêtes sur le cautionnement par le biais des preuves créées par la police, ce qui contribue à l’incarcération massive avant le procès au Canada. Ainsi, le maintien de l’ordre contribue à la création de la « criminalité »” et ce sont les Autochtones, les Noirs et les personnes « vulnérables » qui sont criminalisés de manière disproportionnée et emprisonnés. L’article conclut en rassemblant les moyens par lesquels les preuves générées par la police construisent la criminalité et illustrent la manière dont les audiences de libération sous caution sont lourdement pondérées en faveur de l’État.
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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 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".