Measuring the intellectual diversity encoded in publication records as a proxy to the degree of interdisciplinarity has recently received considerable attention in the science mapping community. The present paper draws upon the use of the Stirling index as a diversity measure applied to a network model (customized science map) of research profiles, proposed by several authors. A modified version of the index is used and compared with the previous versions on a sample data set in order to rank top Hungarian research organizations (HROs) according to their research performance diversity. Results, unexpected in several respects, show that the modified index is a candidate for measuring the degree of polarization of a research profile. The study also points towards a possible typology of publication portfolios that instantiate different types of diversity.
Diversity index Polarization index Science mapping ISI subject categories Hungary Research organizations
This is a preview of subscription content, log in to check access.
We acknowledge the financial support from the Future and Emerging Technologies (FET) programme within the Seventh Framework Programme for Research of the European Commission, under the FET- Open grant number 233847 (Dynanets project, www.dynanets.org).
Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal Complex Systems, 1695.Google Scholar
Korenius, T., Laurikkala, J., & Juhola, M. (2007). On principal component analysis, cosine and Euclidean measures in information retrieval. Information Sciences,177(22), 4893–4905.CrossRefMATHMathSciNetGoogle Scholar
Leydesdorff, L., & Rafols, I. (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology,60(2), 348–362.CrossRefGoogle Scholar
Leydesdorff, L., & Rafols, I. (2010). Indicators of the Interdisciplinarity of Journals: Diversity, Centrality, and Citations. Arxiv preprint arXiv:1003.3613.Google Scholar
Porter, A., Cohen, A., Roessner, D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics,72(1), 117–147.CrossRefGoogle Scholar
Porter, A., & Rafols, I. (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics,81(3), 719–745.CrossRefGoogle Scholar
Porter, A., & Youtie, J. (2009). How interdisciplinary is nanotechnology? Journal of Nanoparticle Research,11(5), 1023–1041.CrossRefGoogle Scholar
R & D Core Team (2009). R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing. ISBN 3-900051-07-0, URL
Rafols, I.; Porter, A., & Leydesdorff, L. (2009). Science overlay maps: a new tool for research policy and library management. Arxiv preprint arXiv:0912.3882.Google Scholar
Rafols, I., & Meyer, M. (2007). How cross-disciplinary is bionanotechnology? Explorations in the specialty of molecular motors. Scientometrics,70(3), 633–650.CrossRefGoogle Scholar
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics,82(2), 263–287.CrossRefGoogle Scholar
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface,40(15), 707.CrossRefGoogle Scholar