Conceptualizing Capacity: Interpreting Canadaâs Qualified Ratification of Article 12 of the UN Disability Rights Convention
Bibliographic record
Abstract
During the negotiations leading up to the United Nations Convention on the Rights of Persons with Disabilities (CRPD), States Parties vigorously debated the scope of Article 12, which establishes legal capacity for persons with disabilities “on an equal basis with others in all aspects of life.” The ambiguity of Article 12 has led to many interpretations that have been the subject of debate among human rights activists and academics. Developments in the jurisprudence and legislative reforms across several jurisdictions indicate that governments and courts have begun to grapple with what recognizing the right to legal capacity for persons with disabilities requires. The purpose of this paper is to examine whether Article 12 imposes an obligation on States Parties to use supported decision-making as an alternative to substituted decision-making, the system in place in most jurisdictions throughout the world. It is argued that the drafters of Article 12 intended to set out a strong presumption of capacity and to permit substituted decision-making only in rare circumstances. This paper uses Canada as an example of a jurisdiction that will need to contend with the legislative implications of Article 12 in light of its existing domestic laws.
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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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.065 |
| Scholarly communication | 0.020 | 0.008 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.011 |
| 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".