R. v. Sharma: Reckoning with Destabilizing Truths in Constitutional Equality Adjudication
Bibliographic record
Abstract
The Supreme Court of Canada’s 2022 decision in R. v. Sharma provides a window on contemporary but divergent judicial approaches to systemic racism in the criminal legal system and how these inform equality challenges based on race. The Sharma majority follows a trend identified by Efrat Arbel in recognizing the “crisis” of Indigenous mass incarceration using language which diffuses the causes of the crisis and does not generate urgent redress. However, in some cases, including in the Sharma dissent, recognition by judges can be an acceptance of accountability as part of the system which has produced these effects. We then argue that claims like Sharma’s can be profoundly destabilizing in a variety of ways — these claims implicate judges as key players in the criminal legal system, they challenge doctrinal and philosophical commitments to individual culpability and blame, and they also create anxiety about the appropriate institutional roles of courts and judges. The Sharma dissent might also, in a contradictory way, restabilize by bringing some radical claims about the criminal legal systems into the embrace of doctrine. We ask how the courts have reckoned with the reality of systemic racism in the criminal legal system and Indigenous mass incarceration as equality matters, noting that section 15 has been avoided in some cases and evaded through evidentiary issues in others. However, we suggest that in the contemporary context, to completely avoid the issue might cause legitimacy problems for courts. While litigation and courts are not likely to be the vehicle for eliminating either Indigenous mass incarceration or systemic racism in our criminal legal system, they can be part of wider shifts in discourse and policy which show greater promise for lasting change.
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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 | 0.002 | 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 | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".