Learning About Real Experiences From Real Users: A Blueprint for Participatory Accessibility Testing
Bibliographic record
Abstract
Although it is crucial for libraries to meet required online accessibility standards (e.g., Web Content Accessibility Guidelines 2.0), compliance with these technical standards does not guarantee optimal or equitable experiences for all library users who interact with online spaces or materials. Recent literature on accessibility testing has acknowledged the value of including people with disabilities in testing and designing digital objects and spaces. This thinking aligns with the library-based user experience (UX) principle that talking directly to users about their experiences using library services and resources is the most effective way to understand and thereby improve the overall library experience. In 2020, the UX Group at Western Libraries undertook a pilot accessibility testing initiative to plan, design, and deliver participatory accessibility testing with campus community members who had self-identified as living with a range of disabilities. Three accessibility tests were designed to assess five distinct digital objects, and 14 testing sessions were completed with eight participants. A semi-structured and participatory testing method allowed participants to freely interact with the testing objects, provide detailed feedback regarding their experiences using the objects, and recommend improvements to elements they found less accessible. This article includes an overview of considerations and challenges of the initiative as well as lessons learned in the process of securing funding, recruiting participants, designing the tests, and conducting testing. We reflect on the value of participatory accessibility testing and make recommendations for conducting similar projects at other libraries.
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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 | 0.231 | 0.168 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.017 | 0.051 |
| Scholarly communication | 0.017 | 0.028 |
| Open science | 0.008 | 0.041 |
| Research integrity | 0.007 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".