AMELIORATIVE PROGRAMS AND THE CHARTER: REFLECTIONS ON THE SECTION 15(2) LANDSCAPE SINCE R V KAPP
Bibliographic record
Abstract
Section 15(2) of the Canadian Charter of Rights and Freedoms affirms the importance of ameliorative laws and programs in the pursuit of substantive equality. In its 2008 decision in R v Kapp, the Supreme Court of Canada interpreted section 15(2) as having independent force to “save” suspect distinctions in government laws, programs or activities that have an ameliorative purpose from scrutiny under section 15(1) or section 1 of the Charter when certain conditions are met. Following Kapp, advocates and commentators expressed various concerns about the new framework for section 15(2) of the Charter. This paper reflects on four of these concerns in light of the small, but growing, body of jurisprudence on section 15(2) that has emerged in the years since the Kapp decision: (1) the application of the Kapp analysis in cases alleging a law or program with an ameliorative purpose is underinclusive; (2) the lack of guidance on what constitutes an “ameliorative law, program or activity” for the purposes of section 15(2); (3) the proper relationship between the section 15(2) analysis and consideration of ameliorative purpose and effects at the section 15(1) stage; and, (4) deference and justification under section 15(2). The paper concludes that there are significant ongoing uncertainties with the Kapp framework for section 15(2), and suggests that section 15(1) of the Charter can protect ameliorative laws and programs in a more principled and equality-enhancing manner than the Kapp framework.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.073 |
| Scholarly communication | 0.027 | 0.012 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.020 | 0.034 |
| 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".