Disability and Employment Policy in Canada: National Policy Variation for Working Age Individuals
Bibliographic record
Abstract
Abstract This article analyses and compares disability policies for working-age individuals in Canada with a focus on the mode of policy provision and type of measure to determine the degree to which direct funding is used in this country. To consider policy diversity in this federal system, policies are compared using a mixed-methods approach. Using quantitative methods, federal, provincial and territorial policies are first compared using hierarchical cluster analysis. This provides evidence of three distinct clusters in Canada according to policy provision and measure type. In a second, qualitative analysis, the disability strategies of four provinces’ (British Columbia, Ontario, Newfoundland and Labrador and Quebec) are compared, to determine over arching policy orientations. Findings indicate that policy provision in Canada largely favours money over services. Furthermore, most provinces emphasize either health or integration measures over substantive measures. Despite these commonalities, significant variation persists across Canada. This extends to poverty and disability reduction strategies with two of the four provinces having a broader orientation while the other two provinces focus specifically on employment as a means of social inclusion. The article concludes with a discussion on the state of employment policies for individuals with a disability in Canada.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".