Skills gaps, underemployment, and equity of labour-market opportunities for persons with disabilities in Canada
Bibliographic record
Abstract
This report is one of a series that explore a number of the most important issues currently impacting the skills ecosystem in Canada. While people with disabilities can achieve socially integrated, financially independent lives through secure, well-paid employment, they are often trapped in low-skill jobs at high risk of automation. In Canada, persons with disabilities typically earn lower wages and are more precariously employed than the average worker. Examining the reasons that people with disabilities are underemployed reveals difficulties finding work and, once employed, difficulties requesting and getting the support they need to advance to their careers. Social stigma, a lack of understanding, and a lack of supports at many life stages further compounds the challenges that persons with disabilities face. In this report, the authors underscore the importance of training opportunities that are well aligned with the skills likely to be in high demand in the future. In particular, research suggests that the transition between school and work appears to be a major challenge for persons with disabilities. Educational institutions and employers could leverage this transition into an opportunity, providing persons with disabilities skills, competencies, and credentials (persons with mild disabilities are already well-educated) to connect into jobs in high growth industries experiencing a need for workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".