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Research in the social sciences has shown that there are gender differences in the selection of research methods, with women often opting for qualitative methods while men prefer quantitative methods. However, it is important to consider that research methods are generally chosen based on the research topic. To figure out the influence of gender on research method selection, a study was conducted in the field of library and information science , using a more fine-grained method classification system and an automatic classification model called CogFT, which is based on full-text cognition. The findings showed that women tend to use interviews while men prefer theoretical approaches, across a range of topics. Insights into the specific research design processes that contribute to gender differences in method selection are offered and ways to promote gender inclusivity and equality in academia by considering research method use and guidance are suggested. Introduction Gender disparities are evident in the scientific community, with women being underrepresented in terms of scientific output, academic rank, and influence (Holman, Stuart-Fox, & Hauser, 2018; Larivière, Ni, Gingras, Cronin, & Sugimoto, 2013; Mairesse & Pezzoni, 2015). These disparities cannot be entirely attributed to physical differences or external prejudice against women (Justman & Méndez, 2018; Lindberg, Hyde, Petersen, & Linn, 2010). One major factor contributing to this issue is the differing career preferences and choices of men and women. In academia, these differences manifest themselves in varying research field and topic choices (Ceci & Williams, 2011; Thelwall, Bailey, Tobin, & Bradshaw, 2019). For instance, fewer women than men choose to pursue further education in areas that need advanced math skills (Ceci, Ginther, Kahn, & Williams, 2014). The gender difference in topic selection also varies across disciplines; for example, women in education prioritize primary and secondary schooling, whereas women in management emphasize social and human-centered research (Scharber, Pazurek, & Ouyang, 2019; Wullum Nielsen & Börjeson, 2019). Several studies attempted to explain the differences in topic choice by the dimension of interest, i.e., women's interests are typically tied to people, whereas men's interests are usually related to things (Su, Rounds, & Armstrong, 2009; Woodcock et al., 2013). However, a single viewpoint cannot uniformly explain every field, as demonstrated by the underrepresentation of women in computer science sub-fields such as human-computer interaction and natural language processing, which both involve people (Su & Rounds, 2015). Women and men might choose topics differently, which in turn influences how they choose research methodologies. Evidence suggests a connection between the gender of the author and the method of study (Diaz-Kope, Miller-Stevens, & Henley, 2019; Williams, Kolek, Saunders, Remaly, & Wells, 2018). Women prefer qualitative methods, whereas men typically utilize quantitative methods to problem-solving (Ashmos Plowman & Smith, 2011; Nunkoo, Thelwall, Ladsawut, & Goolaup, 2020). From a social epistemological perspective, the reason for the differences between female and male research paradigms is that women and men may perceive the world in different ways (Oakley, 2000; O'Shaughnessy & Krogman, 2012; Rolin, 2004), which challenges the research epistemology and the notion that research methods are gender neutral but illustrates that social dimensions, such as gender, influence the production of knowledge in the sciences (Diaz-Kope et al., 2019; Rolin, 2004; Williams et al., 2018). The present study explores gender differences in research topics and methods within a particular field to analyze and understand the potential reasons for gender imbalances in academia and assess the impact of gender on knowledge creation. Section snippets Problem statement While the conventional view is that research methods are chosen based on the nature of research topic and objectives (Creswell, 1994, Creswell, 2003), suggesting gender neutrality of method selection, research in certain social science fields has shown that gender may also play a role in methodological design (Diaz-Kope et al., 2019; Williams et al., 2018). The contradiction between theory and findings may be challenged by the fact that these studies do not adequately consider the influence of Gender differences among scholars in specific fields Gender differences are prevalent in all fields and the manifestation of gender differences varies from area to area (Thelwall, 2020). In most STEM (Science Technology Engineering Mathematics) fields where mathematics is the core foundation, such as physics and computer science, men are significantly better represented than women, while in fields where mathematics is used as a secondary tool, such as psychology and social sciences, female practitioners tend to outnumber men (Ceci et al., 2014; Data sources The data used in this study came from 5281 research articles published in Journal of the Association for Information Science and Technology ( JASIST ), Journal of Documentation ( JDoc ) and Library and Information Science Research ( LISR ) from 1990 to 2019. These three journals were selected for their status as well-established, authoritative, international LIS core journals and their unique and distinct focus on LIS research. JASIST's research focuses on a range of processes, from the production of Results This study focuses mainly on first authors, as they were deemed to play a key role in research topic and method selection (Lu, Zhang, Xiao, & Ding, 2022). Only 281 papers (5.32%) had a discrepancy between the first and corresponding authors, indicating minimal impact of disagreements between the main contributor and the supervisor on the research's development and design. Of the 5281 first authors of LIS journal papers examined, 5273 (99.84%) were gender-identifiable, with 3199 (60.67%) male Analysis of potential reasons for gender differences The gender differences in the research topic selection in the field of LIS may be shaped by the interdisciplinary nature of the discipline itself. According to Järvelin and Vakkari (2021), the focus of LIS research has evolved over time, with scholars increasingly collaborating with other fields to study major research topics. For example, the topic of information retrieval/models and algorithms is a product of collaboration with the field of computer science, and the topic of health Conclusion and future work This study highlights the significant differences between female and male scholars in the use of research methods, even in different topics or journals. While previous research has suggested a general preference for qualitative methods among women and quantitative methods among men, a more nuanced picture where women tend to prefer interviews, while men tend to prefer theoretical approaches was revealed. This discovery opens up new avenues for investigating the underlying causes of these Funding This work was supported by the National Natural Science Foundation of China (Grant No. 72074113 ) and Key Project of Jiangsu Provincial Social Science Foundation (Grant No. 20TQA001 ). Acknowledgments Thanks are due to Professor Heting Chu for providing the annotated data of research methodology for this study. Chengzhi Zhang is a professor of Department of Information Management, Nanjing University of Science and Technology, China. He received his PhD of information science from Nanjing University, China. His current research interests include scientific text mining, knowledge entity extraction and evaluation, and social media mining. He has published more than 100 publications, including JASIST , Aslib JIM , JOI , OIR, SCIM , ACL , NAACL , etc. He serves as Editorial Board Member and Managing Guest Editor References (67) H. Chu Research methods in library and information science: A content analysis Library & Information Science Research (2015) H. Chu et al. Research methods: What’s in the name? Library & Information Science Research (2017) R. 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Finally, in order to take full advantage of the authors’ research interests, we improve the original label propagation method to guarantee good performance in our dataset. Research article Using the full-text content of academic articles to identify and evaluate algorithm entities in the domain of natural language processing Journal of Informetrics, Volume 14, Issue 4, 2020, Article 101091 Show abstract In the era of big data, the advancement, improvement, and application of algorithms in academic research have played an important role in promoting the development of different disciplines. Academic papers in various disciplines, especially computer science, contain a large number of algorithms. Identifying the algorithms from the full-text content of papers can determine popular or classical algorithms in a specific field and help scholars gain a comprehensive understanding of the algorithms and even the field. To this end, this article takes the field of natural language processing (NLP) as an example and identifies algorithms from academic papers in the field. A dictionary of algorithms is constructed by manually annotating the contents of papers, and sentences containing algorithms in the dictionary are extracted through dictionary-based matching. The number of articles mentioning an algorithm is used as an indicator to analyze the influence of that algorithm. Our results reveal the algorithm with the highest influence in NLP papers and show that classification algorithms represent the largest proportion among the high-impact algorithms. In addition, the evolution of the influence of algorithms reflects the changes in research tasks and topics in the field, and the changes in the influence of different algorithms show different trends. As a preliminary exploration, this paper conducts an analysis of the impact of algorithms mentioned in the academic text, and the results can be used as training data for the automatic extraction of large-scale algorithms in the future. The methodology in this paper is domain-independent and can be applied to other domains. Chengzhi Zhang is a professor of Department of Information Management, Nanjing University of Science and Technology, China. He received his PhD of information science from Nanjing University, China. His current research interests include scientific text mining, knowledge entity extraction and evaluation, and social media mining. He has published more than 100 publications, including JASIST , Aslib JIM , JOI , OIR, SCIM , ACL , NAACL , etc. He serves as Editorial Board Member and Managing Guest Editor for 10 international journals ( Heliyon , Patterns , IPM , OIR , TEL , IDD , JDIS , DIM , DI , FRMA etc.) and PC members of several international conferences in fields of natural language process and scientometrics. Currently, he is focusing on scientific text mining, knowledge entity extraction and evaluation, and social media mining. He is also a visiting scholar in the School of Information Sciences (iSchool) at the University of Pittsburgh and in the Department of Linguistics and Translation at the City University of Hong Kong. Siqi Wei is a current graduate student in library and information science at Nanjing University of Science and Technology. She received her bachelor's degree in information management and information systems from Nanjing University of Information Science and Technology. Her research interests include scientometrics, research methods and natural language processing. She has also published in the International Conference on Scientometrics and Informetrics ( ISSI ). Yi Zhao is currently a PhD student in the School of Economics and Management, Nanjing University of Science and Technology. He received his master's degree of economics from Hohai University, Nanjing, China in 2019. His research interests mainly focus on science of science and text mining. He is a visiting scholar in Yonsei University. He has published in Technological Forecasting and Social Change , Data and Information Management , Data Intelligence , and in the International Conference on Scientometrics and Informetrics ( ISSI ). Liang Tian is a current graduate student in library and information science at Nanjing University of Science and Technology. He received his bachelor's degree in information management and information system from Anhui University. His research interests include scientometrics, research methods and natural language processing. He has published in Scientometrics , Knowledge Organization, Data and Information Management and in the International Conference on Scientometrics and Informetrics ( ISSI ). View full text © 2023 Elsevier Inc. All rights reserved. About ScienceDirect Remote access Shopping cart Advertise Contact and support Terms and conditions Privacy policy We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies . Copyright © 2023 Elsevier B.V. or its licensors or contributors. ScienceDirect® is a registered trademark of Elsevier B.V. ScienceDirect® is a registered trademark of Elsevier B.V.
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