The memory of science: Inflation,myopia, and the knowledge network |
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| Authors: | Raj K Pan Alexander M Petersen Fabio Pammolli Santo Fortunato |
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| Institution: | 1. Department of Computer Science, Aalto University School of Science, P.O. Box 15400, FI-00076, Finland;2. Ernest and Julio Gallo Management Program, University of California, Merced, CA 95343, United States;3. Department of Management of Complex Systems, School of Engineering, University of California, Merced, CA 95343, United States;4. Department of Management, Economics, and Industrial Engineering, Politecnico di Milano, Milan 20156, Italy;5. CADS, Center for Analysis, Decisions, and Society, Human Technopole, Milan 20157, Italy;6. Center for Complex Networks and Systems Research, School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA;7. Indiana University Network Science Institute (IUNI), Indiana University, Bloomington, IN, USA |
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| Abstract: | Scientific production is steadily growing, exhibiting 4% annual growth in publications and 1.8% annual growth in the number of references per publication, together producing a 12-year doubling period in the total supply of references, i.e. links in the science citation network. This growth has far-reaching implications for how academic knowledge is connected, accessed and evaluated. Against this background, we analyzed a citation network comprised of 837 million references produced by 32.6 million publications over the period 1965–2012, allowing for a detailed analysis of the ‘attention economy’ in science. Our results show how growth relates to ‘citation inflation’, increased connectivity in the citation network resulting from decreased levels of uncitedness, and a narrowing range of attention – as both very classic and very recent literature are being cited increasingly less. The decreasing attention to recent literature published within the last 6 years suggests that science has become stifled by a publication deluge destabilizing the balance between production and consumption. To better understand these patterns together, we developed a generative model of the citation network, featuring exponential growth, the redirection of scientific attention via publications’ reference lists, and the crowding out of old literature by the new. We validate our model against several empirical benchmarks, and then use perturbation analysis to measure the impact of shifts in citing behavior on the synthetic system's properties, thereby providing insights into the functionality of the science citation network as an infrastructure supporting the memory of science. |
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| Keywords: | Citation network Reference distance Models of science Attention economy Monte Carlo simulation Citation inflation |
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