Grounds-Based Distinctions: Contested Starting Points in Equality Law
Bibliographic record
Abstract
Over the past five years, the Supreme Court of Canada has continued to grapple with the meaning of constitutional equality and discrimination. In this regard, there is a clear consensus that the Court should follow a two-step test to assess violations of section 15(1) of the Canadian Charter of Rights and Freedoms. First, the Court must identify a grounds-based distinction and, second, determine whether the distinction violates substantive equality. While both parts of the test present interconnected conceptual and contextual challenges, this article focuses on how the Court has applied the first step of the section 15 equality analysis. Recent case law reveals a deeply divided Court. First, fundamental differences are apparent with respect to whether grounds-based distinctions may be understood as inextricably embedded in legislative schemes. Second, the justices diverge on the exigencies of proving adverse impact discrimination. Legal technicalities, comparator group formalities, and fear of imposing any positive rights obligations on governments obscure critical dimensions of the disproportionate effects of law. Third, the association of adverse impact with unintentional discrimination risks overlooking the importance of the actual knowledge of disparities in the effects of laws and policies. Finally, the complex realities of intersectionality, while recognized by some justices, continue to remain on the periphery of equality rights doctrine. While the second step of the equality analysis engages more directly with an assessment of the contextual realities of substantive inequality, it is critical to ensure that courts reach this stage of the analysis and that it is not thwarted or obstructed by narrow and formalistic approaches to identifying grounds-based distinctions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.032 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.020 | 0.114 |
| Scholarly communication | 0.027 | 0.026 |
| Open science | 0.006 | 0.013 |
| Research integrity | 0.013 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".