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Record W2888264706 · doi:10.1002/leap.1197

An assessment of the accessibility of PDF versions of selected journal articles published in a WCAG 2.0 era (2014–2018)

2018· article· en· W2888264706 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLearned Publishing · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Accessibility for Disabilities
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComputer scienceWorld Wide WebScreen readerWorkflowAdobeInformation retrievalVisually impairedMultimediaLibrary scienceDatabaseHuman–computer interaction

Abstract

fetched live from OpenAlex

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.015
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.002
Scholarly communication0.0020.013
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.035
GPT teacher head0.369
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it