Access for Whom? An Examination of Public-Facing Accessibility Practices in Library Accessibility Alliance Members’ Open Access Institutional Repositories
Bibliographic record
Abstract
Introduction: Open access aims to provide access to research and scholarship without barriers. An important tool in this process has been institutional repositories (IRs), which disseminate and preserve open scholarship. The goal of this research project was to examine the extent to which IRs in the U.S. are incorporating publicfacing accessibility practices to make their open access works accessible to users of all abilities. Method: This environmental scan reviews the IRs of Library Accessibility Alliance member institutions to identify the prevalence of accessibility practices across those IRs, including contact information, accessibility statements, instructions for submitters, and accessibility-related metadata. Results: This environmental scan found that all but two institutions offered contact information, an avenue for requesting remediation and asking questions. Just over half of the institutions offered IR accessibility documentation, and many linked to other institutional accessibility documentation. Additionally, slightly under a quarter of the institutions provided support for researchers hoping to make their submissions accessible, and three included accessibility information in item-level metadata. Conclusion: While many IR teams are taking some steps to ensure that their IRs are accessible, many accessibility features are not standard across the IRs examined in this study, which suggests the potential for future improvement. Expanded adoption of accessibility best practices would improve access to IR materials and help achieve the ultimate goals of the open access movement.
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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 | 0.028 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".