Accessibility Metadata: Making accessible content in repositories discoverable
Bibliographic record
Abstract
(Watch the RECORDING.) Ensuring inclusive access to scientific output and barrier-free participation in the scientific process for everyone, accessibility is to be regarded as an important component of Open Science. Providing accessibility of research publications, data, infrastructures and communications remains a challenging task, although accessibility considerations within the open science ecosystem have advanced notably in recent decades. Inclusive publishing has a significant impact on the creation of accessible content. In order for existing accessible (or low-barrier) content to be used effectively, it must be made discoverable for users through accessibility metadata. The importance of accessibility metadata and the practical approaches to their implementation are gaining increasing international attention. Significant progress can be observed in the publishing landscape in Canada, Italy, France, the USA and other countries. In the case of e-books, accessibility metadata are also crucial for compliance with the European Accessibility Act, which requires that e-books be made discoverable “by providing information through metadata about their accessibility features”. In the library field, a new network – the Accessibility Metadata Network – was established in 2024, highlighting the relevance of the topic. With regard to repositories, this issue still represents a significant gap, despite its recognized importance. By implementing accessibility metadata, the institutional repository of the University of Vienna PHAIDRA is taking a promising new path. The presentation is intended to raise awareness of accessibility metadata. It clarifies the meaning and functions of accessibility metadata. Next, the presentation gives an overview of various metadata standards and schemas that offer frameworks for tagging accessible resources – such as Schema.org Accessibility Properties for Discoverability Vocabulary, ONIX, MARC21 or IPTC-IIM – along with the options they provide for this purpose and the crosswalks facilitating interoperability. Furthermore, the challenges related to accessibility metadata are addressed. On the one hand, accessibility metadata should be able to describe all relevant accessibility features of an object, requiring detailed and complex information. On the other hand, it is crucial that this information is comprehensible and usable for end users. Moreover, information about a resource's accessibility should be reliably transmitted among all stakeholders and accurately mapped between different metadata standards. In conclusion, the solutions in the PHAIDRA repository and lessons learned are presented.
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.015 | 0.041 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.039 | 0.025 |
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".