Web Accessibility in Higher Education Sector
Bibliographic record
Abstract
90% of websites are inaccessible to people with disabilities who rely on assistive technology (Cahalane, 2018). People who are color-blind, legally blind, and visually and physically impaired are facing issues accessing higher education institution websites. One in five Canadians aged 15 years and over had a disability. (Morris et al., 2018) More than 1.5 million people have hearing difficulties, 375,000 are deaf, and more than 800,000 are blind or partially sighted. This research study examines accessibility and inclusivity in higher education websites during the 21st century. (Lepore & Kearnan, 2022) This research is conducted from a tech-user point of view. Incorporating evidence from literature reviews and studying 7+ universities in Canada through point-based criteria (1-5) and different evaluation software to conclude whether a website is accessible, the level of accessibility present, and potential areas for improvement by implementing some Artificial Intelligence (AI) features.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".