The research space: using career paths to predict the evolution of the research output of individuals, institutions, and nations
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In recent years scholars have built maps of science by connecting the academic fields that cite each other, are cited together, or that cite a similar literature. But since scholars cannot always publish in the fields they cite, or that cite them, these science maps are only rough proxies for the potential of a scholar, organization, or country, to enter a new academic field. Here we use a large dataset of scholarly publications disambiguated at the individual level to create a map of science—or research space—where links connect pairs of fields based on the probability that an individual has published in both of them. We find that the research space is a significantly more accurate predictor of the fields that individuals and organizations will enter in the future than citation based science maps. At the country level, however, the research space and citations based science maps are equally accurate. These findings show that data on career trajectories—the set of fields that individuals have previously published in—provide more accurate predictors of future research output for more focalized units—such as individuals or organizations—than citation based science maps.
KeywordsMaps of science Research policy Innovation policy Career paths Scientograms RCA
Mathematics Subject Classification68U35 94A17 05C90 91D30 68R10
JEL ClassificationI230 I280 94A17
M.G and C.H were supported by the Massachusetts Institute of Technology MIT Media Lab Consortia and MIT Chile Seed Fund. M.G was supported by the Universidad de Playa Ancha, Chile (ING01-1516) and the Universidad Técnica Federico Santa María, Chile (PIIC). D.H was supported by the Marie Curie International Outgoing Fellowship within the EU 7th Framework Programme for Research and Technical Development: Connecting_EU!—PIOF-GA-2012-328828. M.M was supported by Basal Project FB-0821. C.H was supported by the Metaknowledge Network at the University of Chicago.
- Cybermetrics Lab. (2015). About Us | Ranking Web of Universities. Retrieved February 25, 2016, from http://webometrics.info/en/About_Us.
- DataViva. (2016). Retrieved February 3, 2016, from http://en.dataviva.info/.
- Guevara, M., & Mendoza, M. (2013). Revealing comparative advantages in the backbone of science. In Proceedings of the 2013 workshop on computational scientometrics: Theory and applications (pp. 31–36). New York, NY: ACM. doi: 10.1145/2508497.2508503.
- Moya-Anegón, F., Vargas-Quesada, B., Herrero-Solana, V., Chinchilla-Rodríguez, Z., Corera-Álvarez, E., & Munoz-Fernández, F. J. (2004). A new technique for building maps of large scientific domains based on the cocitation of classes and categories. Scientometrics, 61(1), 129–145. doi: 10.1023/B:SCIE.0000037368.31217.34.CrossRefGoogle Scholar
- Neffke, F., Otto, A., & Hidalgo, C. A. (2016). The mobility of displaced workers: How the local industry mix affects job search strategies. Retrieved from http://www.frankneffke.com/files/NeffkeOttoHidalgo_DisplacedWorkers.pdf.
- Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799–813. doi: 10.1002/(SICI)1097-4571(1999)50:9<799:AID-ASI9>3.0.CO;2-G.CrossRefGoogle Scholar