The Earnings and Employment Outcomes of the 2005 Cohort of Canadian Postsecondary Graduates with Disabilities
Bibliographic record
Abstract
Canada's fear of future skill and labor shortages has brought youth with disabilities to the forefront of public policy. Many universities are now reporting that an increased proportion of their graduating students identify as having a disability, and as a result, educational achievement-based programs designed to accommodate students' needs are growing across campuses. Despite recent attention by policymakers on improving accessibility standards and increasing employer incentives, young Canadians with disabilities continue to face barriers in their transitions to the workforce. The nature and extent of the early workforce inequalities faced by postsecondary graduates with disabilities remains unclear. This paper draws on the 2005 cohort of Statistics Canada's National Graduates Survey to examine the early workforce outcomes of postsecondary graduates with disabilities. Contrary to theories of human capital, the results reveal significant earnings gaps between graduates with and without disabilities of various fields of study and levels of schooling. Further, graduates with a disability are even more disadvantaged in terms of securing employment, as they were significantly less likely to be employed full-time, and were overrepresented among unemployed and part-time workers across various fields of study and levels of postsecondary education.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".