Invited to dinner, but not to the table: web content accessibility evaluation for persons with disabilities
Bibliographic record
Abstract
Disability is very common and yet not well understood within sub-Saharan African countries. There has been growing attention to the use of research evidence to improve social inclusion of persons living with disabilities. This article reports on a process that can be used to monitor and evaluate evidence databases to encourage improvements in website and content accessibility for people with disabilities. We examined five evidence communities’ online databases by: (1) assessing the accessibility of these website databases; and (2) assessing the resources within these websites. Finally, we aimed to provide feedback from the evaluation to these evidence databases. We carried out a cross-sectional study of the online evidence databases using the Web Content Accessibility Guidelines – a universal standard for web content accessibility assessment. We assessed access to the databases using a purposive sample of 25 resources within them. Resources are meant to improve practice, policy and decision making for all, including people with disabilities. They include systematic reviews, reports and articles. Accessibility is being able to obtain, understand and use resources; addressing barriers that could hinder this is important. Even though these evidence databases are considered as enabling inclusion and diversity within the evidence ecosystem, their contents are not fully accessible to people with disabilities, and they only partially met the recommendations of the Web Content Accessibility Guidelines.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.137 | 0.032 |
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".