Towards transdisciplinary impact of scientific publications: A longitudinal,comprehensive, and large-scale analysis on Microsoft Academic Graph |
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| Institution: | 1. Information Retrieval and Knowledge Mining Laboratory, School of Information Management, Wuhan University, Wuhan, Hubei 430072, China;2. Department of Information Management, Peking University, Beijing 100871, China;1. School of Information Management, Wuhan University, Wuhan, Hubei, China;2. Information Retrieval and Knowledge Mining Laboratory, Wuhan University, Wuhan, Hubei, China;3. Department of Information Management, Peking University, Beijing, China;1. AGH University of Science and Technology, 30 Mickiewicza Ave, Kraków 30-059, Poland;2. VSB Technical University of Ostrava, 17. listopadu 2172/15, Ostrava-Poruba 708 00, Czech Republic;1. Ryerson University;2. Arizona State University;3. Illinois Institute of Technology;4. University of Guelph |
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| Abstract: | This paper studies the transdisciplinary impact of scientific publications with a longitudinal, comprehensive, and large-scale analysis on the Microsoft Academic Graph (MAG) dataset. More specifically, this paper aims to understand to what extent publications in discipline A have impact on discipline B. To this end, we propose a novel method to characterize the degree to which a publication impacts another discipline instead of its original discipline. We consider the ratio of the number of citations in a certain discipline and that in the original discipline. We also adopt an OLS regression to identify the equation between the ratio and the affinity of discipline pair and find a clear positive relation. This inspires us to categorize a publication's degree of transdisciplinarity by setting up two thresholds, the top 95% and the bottom 95% confident interval curve (of the fitted line). Publications above the top 95% curve is categorized as transdisciplinary ones, those below the bottom 95% curve as domain-specific ones, and those between the two curve as normal publications. This categorization does not require any pre-defined framework for transdisciplinarity and offers an automatic way of definition by data distribution itself. We find that sociology, mathematics, physics, and chemistry account for a great proportion of transdisciplinary publications that influence other domains, and that medicine, biology, economics, and geology have the greatest proportion of domain-specific publications that show impact in the original discipline. Moreover, we observe a negative relation between the number of citations and the proportion of transdisciplinary publications. A longitudinal analysis presents that the proportion of transdisciplinary publications shows a slightly increase trend for years. |
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