Sharma: The Erasure of Both Group-Based Disadvantage and Individual Impact
Bibliographic record
Abstract
Sections 7 and 15 of the Canadian Charter of Rights and Freedoms both play a critical role in protecting members of disadvantaged groups from the harms of state action. In R. v. Sharma, released in November 2022, a 5-4 majority of the Supreme Court of Canada dismissed arguments under both sections in a claim that raised the impact on Indigenous offenders of a 2012 law that restricted the availability of conditional sentences. Our focus in this paper is on the doctrinal implications of the majority and dissenting opinions in Sharma for future section 15 and section 7 claims. We discuss four key section 15 issues raised by the majority and dissent’s differing approaches: (1) the role of substantive equality as the purpose of section 15; (2) the test for section 15 breaches, including three purported “clarifications” made by the majority related to causation, context and positive obligations; (3) the application of the section 15 test to adverse effects discrimination claims that are quantitative and qualitative; and (4) grounds and intersectionality. We also examine several issues that arose with respect to the section 7 claim in Sharma: (1) the role of various principles of fundamental justice; (2) the characterization of the purpose of the impugned law when applying those principles; and (3) the majority’s failure to consider the impact of the impugned law on Cheyenne Sharma. We conclude by exploring the interplay between sections 15 and 7 and the important issues that interplay raises for claims going forward.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".