Uncited papers in the structure of scientific communication |
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| Affiliation: | 1. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russian Federation;2. American Association for the Advancement of Science, 1200 New York Ave NW, Washington, 20005, DC, USA;1. Institute for Statistical Studies and Economics of Knowledge, National Research University Higher School of Economics, 20 Myasnitskaya Ulitsa, Moscow, 101000, Russian Federation;2. American Association for the Advancement of Science, 1200 New York Ave NW, Washington, 20005, DC, USA;1. School of Management Science and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China;2. Information School, University of Sheffield, Sheffield S10 2TN, United Kingdom;1. CSIR-National Institute of Science Communication and Policy Research, Dr. K.S. Krishnan Marg,New Delhi 110012, India;2. Academy of Scientific and Innovative Research (AcSIR), Ghaziabad-201002, India;1. School of Information Management, Nanjing University, Nanjing 210023, China;2. School of International and Public Affairs, Shanghai Jiao Tong University, Shanghai 200230, China;1. Department of Library and Information Science, National Taiwan University, Taipei, Taiwan;2. Center for Research in Econometric Theory and Applications, National Taiwan University, Taipei, Taiwan |
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| Abstract: | The paper presents an in-depth study of uncited papers. For that, we explore the documents indexed in the INSPIRE database from 1970 to 2015. Uncited articles represent a complex bibliometric environment in which references are generated. The reference lists of uncited papers form a dynamic system partially responsible for the redistribution of scientific impact. Our task is to quantify the detailed structure of citations and references directly in terms of quantiles. We also study the entropy and the statistical complexity measure of references and citations of papers’ quantiles. We introduce a theoretical framework in which citation distribution is considered an asymptotic distribution of the largest values in a sequence of independent identically distributed random variables. We show that this asymptotic distribution is the generalized extreme-value distribution. Furthermore, we empirically demonstrate that the asymptotic behavior of citation distribution is close to (but not quite) the generalized extreme-value distribution. |
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