Committing to Justice: The Case for Impact of Race and Culture Assessments in Sentencing African Canadian Offenders
Bibliographic record
Abstract
Canadian judges have made notable, although too limited, strides to recognize the unique conditions of Black Canadians in sentencing processes and decisionmaking. The use of Impact of Race and Culture Assessments in sentencing people of African descent has gradually gained popularity since they were first introduced in R v “X.” These reports provide the court with the necessary information about the effect of systemic anti-Black racism on people of African descent and how the experience of racism has informed the circumstances of the offence, the offender, and how it might inform the offender’s experience of the carceral state. This paper lays out the legislative authority for considering systemic and background factors in sentencing African Canadian offenders; analyzes and classifies the relevant case law with a view to establishing a framework for sentencing African Canadian offenders and clarifying our thinking about how impact assessments may advance sentencing goals; and flags some of the outstanding issues that require further study.\nLes juges canadiens ont fait des progrès notables, bien que trop limités, pour reconnaître les conditions uniques des Canadiens noirs dans les processus de détermination de la peine et de prise de décision. L’utilisation des évaluations de l’impact de la race et de la culture dans la détermination de la peine des personnes d’origine africaine a progressivement gagné en popularité depuis qu’elles ont été introduites dans l’affaire R c. « X .» Ces rapports fournissent au tribunal les informations nécessaires sur l’effet du racisme anti-Noir systémique sur les personnes d’origine africaine et sur la manière dont l’expérience du racisme a influencé les circonstances de la perpétration de l’infraction, le délinquant, et comment elle pourrait influencer l’expérience de l’état carcéral du délinquant. Dans le présent article, nous présentons l’autorité législative permettant de prendre en compte des facteurs systémiques et contextuels dans la condamnation des délinquants afro-canadiens; nous analysons et classons la jurisprudence pertinente en vue d’établir un cadre pour la condamnation des délinquants afro-canadiens et de clarifier notre réflexion sur la manière dont les évaluations d’impact peuvent faire progresser les objectifs de condamnation; enfin, nous signalons certaines des questions en suspens qui nécessiteraient une étude plus approfondie.
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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 | 0.024 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.043 | 0.018 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".