From citation metrics to LLM judgement: a GAIDeT-based framework for structured AI disclosure

Nazarovets, Serhii, Suchikova, Yana, Tsybuliak, Natalia and Teixeira da Silva, Jaime A. From citation metrics to LLM judgement: a GAIDeT-based framework for structured AI disclosure., 2026 . In 30th Annual International Conference on Science and Technology Indicators (STI-ENID 2026), Antwerp, Belgium, 9-11 September 2026. [Conference paper]

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English abstract

This conference paper was prepared for the 30th Annual International Conference on Science and Technology Indicators (STI-ENID 2026), held in Antwerp, Belgium, 9-11 September 2026. The paper examines how the Generative AI Delegation Taxonomy (GAIDeT) can function as a semantic layer for machine-readable information about generative AI use in research. It discusses the potential integration of structured GenAI-use metadata into scholarly communication infrastructures and its possible role as contextual information in future GenAI-aware research evaluation systems.

Item type: Conference paper
Keywords: GAIDeT; generative artificial intelligence; AI disclosure; research evaluation; scientometrics; scholarly communication; research metadata; large language models
Subjects: B. Information use and sociology of information > BF. Information policy
I. Information treatment for information services > IE. Data and metadata structures.
L. Information technology and library technology > LL. Automated language processing.
Depositing user: Serhii Nazarovets
Date deposited: 09 Sep 2026 08:49
Last modified: 09 Sep 2026 08:49
URI: http://hdl.handle.net/10760/48506

References

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Thelwall, M., Jiang, X., & Bath, P. A. (2025). Estimating the quality of published medical research with ChatGPT. Information Processing and Management, 62(4), 104123. https://doi.org/10.1016/j.ipm.2025.104123

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