Adverse Impact: The Supreme Court's Approach to Adverse Effects Discrimination under Section 15 of the Charter
Bibliographic record
Abstract
The recognition and remedying of adverse effects discrimination is crucial to the realization of substantive equality. However, the Supreme Court of Canada’s analytical approach to section 15(1) of the Canadian Charter of Rights and Freedoms has made it difficult for equality claimants to mount successful claims of this type. This article comprehensively reviews and critiques the Supreme Court’s section 15(1) adverse effects discrimination jurisprudence in order to identify the barriers. We argue that the Court’s approach to section 15(1) has used direct discrimination as the paradigmatic case, creating an adverse impact on adverse effects discrimination claims. We identify the following problem areas: more burdensome evidentiary and causation requirements; assumptions about choice; reliance on a comparative analysis; acceptance of government arguments based on the “neutrality” of their policy choices; narrow focusing on discrimination as prejudice and stereotyping; and failing to “see” adverse effects discrimination, often because of the size or relative vulnerability of the claimant sub-group. We also examine two adverse effects claims currently before the Supreme Court, Taypotat and Carter, to analyze whether and how these problem areas play out in those appeals. We conclude by exploring how the harms of adverse effects discrimination can be placed on an equal footing with those of direct discrimination.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.008 | 0.011 |
| 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".