Identifying the best web accessibility workflows for legacy archival description data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper examines the challenges and solutions associated with making archival PDF finding aids accessible to blind and low-vision users, particularly those who rely on screen readers. The project, conducted at the University of Toronto, highlights the barriers posed by unstructured PDFs, which fail to meet the various accessibility standards specified in the WWW Consortium’s Web Content Accessibility Guidelines. Three methods were tested to improve accessibility: manual remediation, PDF-to-HTML conversion, and data migration into the Access to Memory (AtoM) platform. The results indicated that both the manual and automated remediation methods were either too costly or ineffective, while the most promising approach involved migrating the description data into AtoM via CSV import, enhancing both accessibility and search functionality. The paper underscores the need for ongoing funding and professional expertise to address web accessibility issues in archival settings and outlines future steps to improve PDF generation within AtoM for broader application.
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.
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 | 0.003 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it