Identifying with Disability: Benefit or Barrier? A Three-Part Study Examining the Impact of Disability on Over-Qualification, Access to Work-Related Training, and Inclusion in University Classrooms
Bibliographic record
Abstract
Despite Canada's commitment against discrimination, workers with disabilities face barriers to labour market participation. Relying on Human Capital Theory, Signaling Theory, and Stigma theory, this three-part study examines impact of disability status in three distinct contexts. Study 1 explores the relationship between disability status and job-qualification mismatches in a representative sample of 7172 respondents from Statistics Canada's General Social Survey (GSS) data bases. Disability status is negatively corelated with horizontal job education match, and positively corelated with perceptions of under-qualification. Study 2 explores the relationship between disability status and work-related training using a series of logistic regressions, and a GSS sample of n =7, 154. Disability status is negatively corelated with the probability of receiving employer-sponsored training. Study 3 tests the impact of disclosing disability related information university project team inclusion decisions. Using a sample of 378 TMU students, Study 3 found that signaling diabetes had no main effect on inclusion ratings, but diabetes-related stigma negatively predicted the inclusion of those with diabetes. Taken together, these studies failed to prove disability status as a consistent disadvantage. Instead, unanticipated results suggest a more complex relationship between disability status and labour market outcomes. I discuss the implications of these findings and suggest directions for future research.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".