Website navigation: Access to online resources to determine eligibility for augmentative & alternative communication (AAC) technology in Canada
Bibliographic record
Abstract
Alternative and augmentative communication (AAC) systems enable interaction by persons with speech impairments, yet access to these devices is limited. In 2019, the Government of Canada introduced "The Accessible Canada Act" to reduce barriers. Availability of information online about AAC systems can reduce barriers to many Canadians who have difficulty attending in-person appointments. While Ontario's Assistive Device Program has been reviewed, other government-funded and charitable organizations across Canada have not been assessed for readability and accessibility. This research aims to evaluate the websites of organizations across Canada that provide AAC technology access, either through equipment loans or financial assistance programs. Forty-three eligible organizations were identified. Web Content Accessibility Guidelines scores (A, AA, and AAA) and four readability scores (Flesch Kincaid Reading Ease, Flesch Kincaid Grade Level, Gunning Fog, and age range) for each website were determined. Thirteen of 43 sites scored below the recommended standard of 75 for WCAG score, and Flesch Kincaid Reading Ease score indicated 86% were more difficult to read than standard recommendations for web content. To enhance equity in AAC device access, online availability of information and forms of government programs and charitable organizations must be easily understood and barrier-free.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".