Functional Accessibility on the Web: How the AODA Fails End Users
Bibliographic record
Abstract
The Accessibility for Ontarians with Disabilities Act, 2005 (AODA) impacts the way persons with disabilities are able to access goods and services in Ontario. With more than 3 million people living with disabilities in Ontario, it is essential to enact the legislation to create equity. The AODA equates “obtainable� with “accessible� without recognizing the stark contrast between having access to something and being able to keep informed because the content is created with persons with disabilities in mind. This paper focuses on the effectiveness of the AODA’s website compliance standards under the Web Content Accessibility Guidelines (WCAG) as required by Ontario Regulation 191/11, section 14 (O. Reg. 191/11s.14). Through an examination of the compliance requirements in comparison to the “Five E� Approach to Policy Analysis it is apparent that the AODA does not create equity or equality, but rather is a tool for the Ontario Government to demonstrate that it is moving toward making Ontario accessible for persons with disabilities. The “Five E� Approach to Policy Analysis of the AODA demonstrates that O. Reg. 191/11 s.14 does not have the proper mechanisms in place to ensure equity in Ontario with regards to websites and internet usage. It is hoped that this study will increase awareness about the discrepancy between what is written in the AODA and the type of accessibility needed for persons with disabilities in Ontario so the Government can move forward towards true equity.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".