Bibliographic record
Abstract
Aim/Purpose: Understanding how Black people view the justice system helps in identifying some areas of friction, and thereby provides critical insights into the measures needed to improve the working and management of the justice delivery process. Background: Access to justice is a fundamental Canadian value. However, evidence shows that Black and racialized communities have been facing systemic racism and discrimination in all forms, and in all phases of the justice system. There is a need to study critical information regarding the inadequacies of the justice system, how they affect Black people, and what can be done to bridge the gap between Black communities and government policies in Quebec. Methodology: Through systematic race-based data collection, this study examines the perception of racial discrimination law enforcement and courts among Black communities in Quebec. Using a total sample of ninety-three (N=93) Black respondents, the study sheds light on Black people perception to identify and respond to issues of social inequities, discrimination, and racism within Quebec society. Both Likert scale and open-ended questions were asked in order to measure perception of justice accessibility, respectful interaction with law enforcement, fair procedure and outcome, language and overall perception of the justice system. Findings: The findings indicate that respondents with personal experience with police officers are less likely perceive access to justice positively. Regardless of their demographic characteristics, the results of this study confirmed that Black people continue to experience various forms of injustice, discrimination, and unfair treatment in Quebec. Impact on Society: Racism affects Black Canadians at every step of the criminal justice system, from policing to pretrial detention to sentencing to prisons. To have a clearer picture of the extent and nature of anti-Black racism in the Canadian criminal justice system, the researchers adopted systematic race-based data collection. Studies similar to the current research are the key opportunity to address anti-Black racism in the justice system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".