Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science (LIS). To assess the influence of this data-driven research paradigm on the LIS discipline, this study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain.
Research methods are essential carriers of knowledge contribution in academic papers. Automatic multi-label classification of research methods can support knowledge services such as method retrieval, review generation, and research intelligence analysis.
arXiv:2112. 04573v2 Announce Type: replace-cross Abstract: As the concept and implementation of cutting-edge technologies like artificial intelligence and machine learning has become relevant, academics, researchers and information professionals involve research in this area.
By Rajesh Kumar Das, Mohammad Sharif Ul Islam
arXiv:2606. 26130v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to guide research methodology, yet their default methodological tendencies under minimal prompting remain unclear.
By Francesca Carlon, Brecht Verbeken, Vincent Ginis, Andres Algaba
arXiv:2204. 08476v2 Announce Type: replace-cross Abstract: In recent years, with the increase of social investment in scientific research, the number of research results in various fields has increased significantly.
By Changwei Zheng, Zhe Xue, Meiyu Liang, Feifei Kou, Zeli Guan
arXiv:2509. 02581v2 Announce Type: replace-cross Abstract: Despite the growing availability of tools designed to support scholarly knowledge extraction and organization, many researchers still rely on manual methods, sometimes due to unfamiliarity with existing technologies or limited access to domain-adapted solutions.
By Azanzi Jiomekong, Hande K\"u\c{c}\"uk McGinty, Keith G. Mills, Allard Oelen, Enayat Rajabi, Harry McElroy, Antrea Christou, Anmol Saini, Janice Anta Zebaze, Hannah Kim, Anna M. Jacyszyn, Gollam Rabby, Dirk Betz, Claudia Biniossek, Sanju Tiwari, S\"oren Auer
The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results. This paper presents an on-going use case developed within the European project LLMs4EU and the ALT-EDIC infrastructure, aimed at adapting foundation models to SSH research practices and supporting tasks such as question answering, comparative document analysis and literature review.
arXiv:2607. 05956v1 Announce Type: new Abstract: The integration of Large Language Models (LLMs) into scientific research workflows, particularly for bibliographic discovery and literature synthesis, raises significant methodological, epistemic and regulatory challenges for the Social Sciences and Humanities (SSH), especially with regard to disciplinary diversity, multilingual access to sources and the evaluation of results.
By Adam Faci, Alessio Miaschi, Anne Combe, Pascal Cuxac, Francesca Frontini, Nicolas Larrousse, St\'ephane Pouyllau
arXiv:2510. 16152v2 Announce Type: replace-cross Abstract: Scientific literature is increasingly fragmented by disciplinary boundaries, specialized terminology, and potentially sparse keyword systems, making it difficult to capture the evolving structure of modern science.
By Mason Smetana, Lev Khazanovich
arXiv:2608. 10740v1 Announce Type: new Abstract: Effective research ideation requires moving beyond a static understanding of prior work to trace how research problems and solutions evolve across the literature.
By Xun Li, Yiying Yang, Pengtao Li, Xiao Yao, Suyu Liu, Xiaoyang Ye, Ziyu Lu, Yuan Yao, Yangning Li, Yinghui Li, Wenhao Jiang
arXiv:2606. 12413v1 Announce Type: cross Abstract: Students at all levels of higher education face a significant barrier in the form of information overload, which often paralyzes the initial stages of the research process and suppresses motivation.
By Andrei Lazarev, Dmitrii Sedov
arXiv:2607. 05420v1 Announce Type: cross Abstract: This study examines how alternative systems of scholarly representation identify and characterize broad public administration (PA) and artificial intelligence related public administration (AI-in-PA) scholarship.
By Shaoming Cheng, Laurie Schintler