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AI in Libraries: Subject Mapping and Interpreting Bibliometric Laws with Contemporary Metrics.

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Název: AI in Libraries: Subject Mapping and Interpreting Bibliometric Laws with Contemporary Metrics.
Autoři: Esh, Manash, Ghosh, Saptarshi
Zdroj: Serials Librarian; 2024, Vol. 85 Issue 5/6, p99-116, 18p
Témata: ARTIFICIAL intelligence, LIBRARY science, PROBABILITY theory, INFORMATION retrieval, BIBLIOMETRICS, INFORMATION resources management
Abstrakt: Incorporating artificial intelligence (AI) into library science has significantly transformed information management and retrieval. This study explores AI in libraries, focusing on subject mapping and bibliometric laws, using data from 2014 to 2023. This paper provides insights into the evolution of scholarly communication, knowledge dissemination, and information retrieval." The study reveals patterns in library and information science distribution, productivity, and impact by examining bibliometric laws such as Bradford's, Lotka's, and Zipf's. This study utilised systematic searches, robust databases, and bibliometric tools to ensure methodological rigour. Seventy-six documents meeting predefined criteria were selected, with Scopus Elsevier as the primary data source. Analysis was performed using Bibliometrix software in RStudio, supplemented by Python code for visualisation and statistical analysis. Bradford's law highlighted the concentration of AI literature in specific journals, while Lotka's Law confirmed the unequal productivity among authors. The study concludes with recommendations for future research, offering a comprehensive understanding of AI's implications in library science. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Incorporating artificial intelligence (AI) into library science has significantly transformed information management and retrieval. This study explores AI in libraries, focusing on subject mapping and bibliometric laws, using data from 2014 to 2023. This paper provides insights into the evolution of scholarly communication, knowledge dissemination, and information retrieval." The study reveals patterns in library and information science distribution, productivity, and impact by examining bibliometric laws such as Bradford's, Lotka's, and Zipf's. This study utilised systematic searches, robust databases, and bibliometric tools to ensure methodological rigour. Seventy-six documents meeting predefined criteria were selected, with Scopus Elsevier as the primary data source. Analysis was performed using Bibliometrix software in RStudio, supplemented by Python code for visualisation and statistical analysis. Bradford's law highlighted the concentration of AI literature in specific journals, while Lotka's Law confirmed the unequal productivity among authors. The study concludes with recommendations for future research, offering a comprehensive understanding of AI's implications in library science. [ABSTRACT FROM AUTHOR]
ISSN:0361526X
DOI:10.1080/0361526X.2024.2444288