Impact of Race and Culture Assessments (IRCAs) in combatting anti-Black racism and reducing recidivism
Bibliographic record
Abstract
The Gladue report, named after R. v. Gladue, is a landmark Supreme Court of Canada case which emphasizes the need to consider unique circumstances faced by Indigenous individuals when determining appropriate sentences. Given the overrepresentation of Black identities at all levels in the justice system, it is argued that the use of pre-sentencing reports referred to as Impact of Race and Culture Assessments (IRCAs), also needs to be comprehensively implemented for Black offenders in Canada. IRCAs are pre-sentencing reports that help sentencing judges better understand the effect of poverty, marginalization, racism, and social exclusion on the offender and their life experiences, and how those factors inform the circumstances of the offender, the offence committed, and the offender’s experience with the justice system. This is significant as it goes beyond a one-size-fits-all punitive justice system that has been ineffective in reducing recidivism. By recognizing the intersections of race, culture, and justice, IRCAs enable judges to make more informed decisions contributing to an equitable consequence for the accused. More importantly, we argue that the insights from IRCAs should be used to connect offenders with culturally reflective wraparound social services upon return into the community to address the root causes in areas of employment, education, and housing that gravitate people towards criminality. By acknowledging historical and systemic biases and tailoring supports to individual identities, life experiences, and community conditions, IRCAs have the potential to transform the criminal justice system through promotion of alternatives to custody that correlates with reductions in recidivism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".