Digital repositories shape how knowledge is organized and accessed, yet their metadata often reproduces linguistic hierarchies. This study analyzes queer and gender-diverse representation in India’s National Digital Library of India (NDLI) and Shodhganga through multilingual keyword searches. Results show English terms like queer, LGBT, and transgender dominate while vernacular South Asian identity terms such as hijra, kothi, aravani, jogappa and mangalamukhi appear far less frequently within repository metadata fields such as titles, abstracts and keywords. To address these inequities, the study proposes the “Queer Indic Term Map” (QITM), a framework linking Indian vernacular terms with inclusive vocabularies like “Homosaurus” and “Gender, Sex and Sexual Orientation” (GSSO). QITM demonstrates how culturally grounded metadata can enhance inclusivity and visibility in national digital repositories.

New roles in AI are emerging throughout Industry, Academia, and Government. From responsible AI management to AI systems engineering, these roles require new skills, many of which align closely with librarian competencies. Roles in AI where librarian skills are especially relevant include organizing information, facilitating documentation, assessing societal impacts, and working with communities of both users and developers. In this paper, we provide mappings from ALA-defined librarian competencies and Library and Information Studies curricula to an inventory of AI competencies. We provide an analysis of five areas of alignment, as in correspondence, including responsible AI and social justice and data engineering and evaluation.

This study compares the Annif-based DESCRIPTOR e-service and GPT-4o for the automatic indexing of Polish library and information science articles using titles and abstracts. DESCRIPTOR e-service scored precision 0.27, recall 0.49, F1 0.34, and mean average precision 0.67; GPT-4o scored 0.27, 0.48, 0.34, and 0.75, respectively. The global Jaccard index was 0.22. Manually created descriptions contained fewer irrelevant terms than those produced by either tool; however, GPT-4o outperformed DESCRIPTOR e-service on this metric. Both tools identified relevant terms that were absent from the manual descriptions. Overall performance is moderate, so further improvements are needed for reliable automatic indexing of Polish resources.

The Library of Congress Genre/Form Terms (LCGFT) vocabulary is among the most prominent genre vocabularies used in library cataloging. To investigate how the current application of LCGFT affects users’ genre access, we gathered data from library patrons, library staff, and parties involved in genre creation and use. The results demonstrate that patrons actively seek out genre materials in libraries, often via library catalogs, and that LCGFT has room for growth and expansion that might enhance patrons’ search experience.

This study investigates whether introducing hierarchical and inclusion relationships to mappings between RDA elements and DC terms/MARC 21 fields can reduce complexity and enhance comprehensibility. The results indicate that applying these relationships elucidates the mapping process, using a proposed data visualization method. Quantitative analysis showed 98.4% and 92.2% consistency in DC and MARC mappings, respectively, with reduced inconsistencies when criteria were adjusted consistently with these relationships. However, certain cases remained unresolved, requiring further investigation into contextual factors affecting mapping inconsistencies. These findings help improve metadata interoperability, helping catalogers understand large-scale mappings.

Generative AI tools are transforming library cataloging by improving efficiency and expanding access to multilingual resources. This study examines the application of AI in automating the transliteration of Wade-Giles romanized titles into Pinyin in MARC records. Three generative AI tools—ChatGPT, Copilot, and Gemini—were evaluated for their efficiency, and they exhibited distinct strengths and limitations. The study highlights persistent challenges, including inconsistent outputs, fluctuating accuracy levels, and the need for data pre-processing. These findings paint a promising future for AI integration in cataloging workflows but also highlight the need for continuous refinement to achieve scalable performance optimization.

This study aimed to catalog five previously undocumented Gəˀəz manuscripts from Central West Ethiopia, focusing on their philological aspects to enhance understanding and preservation efforts. The manuscripts’ physical descriptions, contents, and historical context were explained using a mixed research approach. Fieldwork and inventory methods were employed to collect data, ensuring detailed manuscripts’ external and internal features. The analysis of the manuscript document revealed that all manuscripts were written in vellum, using Gəˀəz and Amharic language. This research contributes significantly to the preservation of Ethiopia’s manuscript heritage and provides valuable resources for scholars and the broader public.

The inter-resource relationship designators listed in RDA: Resource Description and Access are assessed in terms of their wider applicability, by examining the links provided in 500 YouTube video descriptions. In addition to 128 links to other resources on the same topic, 90 links represented intrinsic relationships between resources, all of which could be assigned an RDA designator, although in some cases more specific designators not in RDA could have been used. Overall, the findings suggest RDA provides a robust framework for the description of bibliographic relationships beyond the library setting, at least with respect to internet videos.