Assessing academic impacts of machine learning applications on a social science: Bibliometric evidence from economics |
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| Institution: | 1. School of Information Management, Wuhan University, Wuhan, Hubei, 430072, China;2. Center for the Studies of Information Resources, Wuhan University, Wuhan, Hubei, 430072, China;3. Department of Sociology, University of Chicago, Chicago, IL, 60637, USA;4. Knowledge Lab, University of Chicago, Chicago, IL, 60637, USA;1. The College of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China;2. School of Economics and Management, China University of Geosciences, Beijing 100083, China;3. Key Laboratory of Carrying Capacity Assessment for Resource and Environment, Ministry of Natural Resources, Beijing 100083, China;4. Chinese Academy of Geological Sciences, Beijing 100037, China;1. Max Planck Institute for Solid State Research, Heisenbergstr. 1, Stuttgart 70569 Germany;2. Science Policy and Strategy Department, Administrative Headquarters of the Max Planck Society, Hofgartenstr. 8, Munich 80539 Germany;1. School of Information Management, Sun Yat-Sen University, Guangzhou, 510006, PR. China;2. School of Economics & Management, Nanjing University of Science and Technology, Nanjing, 210034, PR. China;3. Library, Party School of Jiangsu Provincial Committee of CPC, Nanjing, 210034, PR. China;1. Centre for R&D Monitoring (ECOOM), KU Leuven, Leuven, Belgium;2. Department of Management and Production Engineering, Politecnico di Torino, Turin, Italy;3. Department of Management, Strategy and Innovation, KU Leuven, Leuven, Belgium;4. Flanders Business School, KU Leuven, Antwerp, Belgium |
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| Abstract: | Machine learning (ML) methods have recently been applied in diverse fields of study. ML methods provide new toolkits and opportunities for social sciences, but they have also raised concerns with their black-box nature, irreproducibility, and emphasis on prediction rather than explanation. Against this backdrop, we study the bibliometric impact of leveraging ML methods in economics using publications indexed in Microsoft Academic Graph. We use our four-dimensional bibliometric framework by which we gage citation intensity, speed, breadth, and disruption to compare two groups of publications in economics (2001–2020)—those using ML methods and others not. We find that economics papers applying ML methods started to have advantages in citation counts and speed after 2010. Our analysis also shows that they received attention from more diverse research communities and had more disruptive citations over the past two decades. Then, we demonstrate that economics papers using ML methods obtained more disruptive citations within economics than outside. These findings suggest bibliometric advantages for applying ML methods in economics, especially in the recent decade, but we also discuss cautions and potential opportunities missed. |
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