Embedded Barriers and Impending Costs: The Relationship between Disability, Public Schooling, Post-Secondary Education, and Future Income Earnings
Bibliographic record
Abstract
In Canada, access to post-secondary education (PSE), which includes university, college, or apprenticeship programs, is becoming ever more important in terms of securing future employment, long-term health, and economic security. Kirby (2009) points to Canada’s universal level of PSE access; however, also notes how access for students with disabilities continues to be more limited. This article reports on a study that examined the barriers students with disabilities encounter in their pursuit of PSE, as well as how they access PSE, their graduation rates, and their future income earnings. With a focus on education, we grounded this study in critical disability theory to consider how disability is constructed and produced through social, environmental, and economic factors. This study built on earlier research that examined students’ graduation from post-secondary education and explored disabled students’ access to post-secondary education and their future earnings following PSE participation. Using a unique linked dataset between school board and federal data, our study revealed that disabled students are almost twice as likely to not access post-secondary education compared to their non-disabled peers. Across disability status, the outcomes of post-secondary credentials do not appear to result in future income parity, suggesting persistent ableism within the workforce.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".