Critical Comparisons: The Supreme Court of Canada Dooms Section 15
Bibliographic record
Abstract
Comparison has become a central component of the equality analysis under section 15 of the Charter of Rights and Freedoms. While comparison can be a useful tool in understanding inequalities and crafting appropriate remedies, the current understanding of comparison employed by Canadian courts has been reduced to requiring the claimant to describe a single correct comparator group that applies to his or her situation. This restrictive use of comparison revives the formal equality approach rejected by the Supreme Court of Canada 15 years ago, and leads to overly simplistic analyses. It is therefore necessary to rethink the use of comparison and comparator groups in section 15 equality jurisprudence. Following a discussion of the rise of comparator groups under section 15, the Supreme Court of Canada decisions in Granovsky v. Canada (Minister of Employment and Immigration), Auton (Guardian ad litem of) v. British Columbia (Attorney General) and Falkiner v Ontario (Director, Income Maintenance Branch, Ministry of Community and Social Services) are used to demonstrate the problems with the current comparator group approach. The paper ends with some preliminary thoughts on a more flexible and open use of comparison in equality jurisprudence.
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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 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.033 |
| Scholarly communication | 0.016 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| 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".