Advancing Disability Equality Through Supported Decision-Making: The CRPD and the Canadian Constitution
Bibliographic record
Abstract
Canada is known around the world for being a leader in disability rights and a champion of the Convention on the Rights of Persons with Disabilities (CRPD). Notwithstanding this reputation, Canada has failed to fully embrace Article 12, the right to equal protection under the law. Canada has given only a qualified endorsement of supported decision-making, which empowers individuals with mental disabilities to exercise the right to legal capacity by making and communicating decisions for themselves. Canadian law preserves substitute decision-making regimes, which can arbitrarily strip persons with mental disabilities of their decision-making authority. This chapter looks at Canadian federalism (division of powers) as an obstacle to Canada’s effort to implement its international human rights obligations. Taking a fresh approach, the author argues that the federal government could be constitutionally permitted to legislatively redesign legal capacity in a way that enhances the dignity and autonomy interests of people with mental disabilities by ensuring comprehensive Article 12 compliance. This, it is argued, can be done with constitutional authority never before acknowledged but demonstrably defensible on the basis of promoting the full and equal inclusion of persons with mental disabilities in Canadian society as a matter of national concern.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.034 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".