An assessment of the accessibility of PDF versions of selected journal articles published in a WCAG 2.0 era (2014–2018)
Bibliographic record
Abstract
Two hundred portable document format (PDF) articles from four Web of Science‐indexed disability‐related journals were analysed to assess their accessibility. Fifty articles from each journal published between 2014 and 2018 were examined using expert manual inspection, Adobe Acrobat Pro XI, PDF Accessibility Checker 3 and NVDA screen reader. Results show that only 15.5% of the documents were tagged, only 10.5% had alternative text for images, 74.5% had bookmarks to facilitate navigation, and 87% had meaningful titles in their title fields. However, image alternative texts were meaningless, and title fields were not displayed when the document was open. However, all the documents had accessibility permissions enabled; hence, they could be read with Adobe Acrobat Pro XI Read Out Loud feature and NVDA screen reader. All the articles had an alternative HTML version of their full text in the same location on their website as the PDF versions. The inconsistency with which each PDF was produced suggests the need for an improvement in the workflow process to improve accessibility.
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 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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.034 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".