Charting the Landscape of Accessible Education for Post-secondary Students with Disabilities
Bibliographic record
Abstract
This article presents the results of research examining the impact of the Accessibility for Ontarians with Disabilities Act (AODA) on educational accessibility at one university in Ontario, Canada. A longitudinal, qualitative study was conducted to explore how students with and without disabilities, instructors, staff members and administrators perceived the relative accessibility of teaching and learning on campus before, during, and after the implementation of one portion of the AODA legislation. In the first phase of this research, several factors affecting educational accessibility at the study university were noted, including knowledge, attitudes, pedagogical choices, disciplinary features, and institutional practices and characteristics. Participants raised many of these issues in the later phases reported here, although some preliminary changes in awareness and institutional practices are also described. Based on these minimal developments, and on participants’ expressed perceptions of the AODA, we conclude that the legislation has had limited impact on the accessibility of teaching and learning on campus to date. Implications of the findings, potentially applicable in many contexts beyond the Ontario setting where the research was conducted, as well as next steps and recommendations for further research are discussed.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".