Probing The Data: Perspectives on Race Visibility in Canadian Sentencing Proceedings
Bibliographic record
Abstract
This article analyzes interview data from nine Black criminalized individuals and nine defence lawyers (five white, three Black, and one Arab) about the utility of heightened race visibility in sentencing proceedings. The data reveals a schism between these groups, reflecting different responses to what I refer to as “the paradox of visibility.” For Black people, this paradox occurs when an emphasis on race may simultaneously have a deleterious and ameliorating impact on sentencing. Defence lawyers and judges laud the ameliorative potential of race visibility, which obscures the genuine concern shared by criminalized Black individuals about how they believe their Blackness betrays them in the criminal sentencing context. In this regard, the article explores ethical concerns arising from this paradox. It also argues that race-based strategies at sentencing are not a no-cost or low-cost proposition. Indeed, from the criminalized research participants’ point of view, the cost is not only the risk that an emphasis on race may result in a higher sentence, including longer and harsher custodial sentences, but also an affront to their dignity. In contrast, the defence lawyers strongly supported increased racial visibility to combat what they saw as judicial and prosecutorial intransigence to grapple with race in sentencing proceedings. These perspectives are critical for sentencing judges tasked with sentencing Black individuals and for lawyers who are developing and deploying legal strategies to assist their Black clients.
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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 | 0.023 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.054 | 0.019 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".