Lovelace and Law Revisited: The Substantive Equality Promise of Kapp
Bibliographic record
Abstract
The Supreme Court’s decision in R. v. Kapp substantially advances the equality analysis under section 15 by revisiting the test for a section 15 infringement, most recently articulated in Law v. Canada (Attorney General), and by establishing a new role for section 15(2). A fuller understanding of the impact of this landmark decision on ameliorative programs requires an appreciation of the thorny history of section 15 treatment of ameliorative programs. This paper examines the legislative and judicial history of section 15(2) and situates the Kapp decision within it. It further discusses the promise that the Kapp decision holds of a more straightforward substantive equality analysis when addressing challenges to ameliorative programs. While acknowledging Kapp’s potential, this paper also examines fundamental questions left unanswered by Kapp that courts both before and after have grappled with: what role do the contextual factors underlying the human dignity test set out in Law (most particularly the correspondence analysis) now have? How will the “rational connection” test for section 15(2) articulated by the Court in Kapp be applied in future? and how should section 15(2) apply to under-inclusive challenges? On the latter issue, the Ontario Court of Appeal’s solution in Lovelace is proffered as a potential model for how to proceed.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.052 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.015 | 0.021 |
| 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".