Defining Disability: A Comparative Policy Analysis
Bibliographic record
Abstract
This research assesses how Canadian laws define disability and their impact on disability benefits and programs related to the UN Convention on the Rights of Persons with Disabilities (UN CRPD). Canada has committed to ensuring that its federal, provincial, and territorial laws reflect the principles of dignity, equality, and inclusion for persons with disabilities, as outlined in the UN CRPD. However, there is no evidence on how disability is defined across jurisdictions and its implications on programs available to individuals. The study uses a comparative policy analysis to evaluate 48 pieces of legislation and 201 programs and benefits across Canada. Using the READ methodology, the research identifies patterns in how disability is framed, mainly through medical and biopsychosocial models, and analyzes the implications of these frames for policy implementation. The findings reveal that most Canadian legal documents (n=37) align with a medical model, determining eligibility criteria based on impairments. In contrast, fewer documents (n=11) align with a biopsychosocial model, which closely reflects the UN CRPD by considering the interaction between an individual's health condition and societal barriers. The results highlight the need for a common definition of disability across Canadian legislation to ensure equitable access to disability benefits and services. The study concludes with policy recommendations, advocating for the adoption of a common disability definition, the establishment of a standard classification system based on the World Health Organization's International Classification of Functioning, Disability, and Health (ICF), and the alignment of the Canada Disability Benefit's eligibility criteria with the Canada Disability Benefit Act.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".