The causes of gender bias favoring men in scientific and scholarly systems are complex and related to overall gender relationships in most of the countries of the world. An as yet unanswered question is whether in research publication gender bias is equally distributed over scientific disciplines and fields or if that bias reflects a closer relation to the subject matter. We expected less gender bias with respect to subject matter, and so analysed 14 journals of gender studies using several methods and indicators. The results confirm our expectation: the very high position of women in co-operation is striking; female scientists are relatively overrepresented as first authors in articles. Collaboration behaviour in gender studies differs from that of authors in PNAS. The pattern of gender studies reflects associations between authors of different productivity, or “masters” and “apprentices” but the PNAS pattern reflects associations between authors of roughly the same productivity, or “peers”. It would be interesting to extend the analysis of these three-dimensional collaboration patterns further, to see whether a similar characterization holds, what it might imply about the patterns of authorship in different areas, what those patterns might imply about the role of collaboration, and whether there are differences between females and males in collaboration patterns.
Gender bias Co-operation Social networks Co-authorship Collaboration patterns
Mathematical Subject Classification (2000)
62 68 91 94
C0 C02 C3 C31 C46
This is a preview of subscription content, log in to check access.
Part of this work by one of the authors (Kretschmer H) was supported by the 7th Framework Program by the European Commission, SIS-2010-126.96.36.199. Project full title: “Academic Careers Understood through Measurement and Norms “, Project acronym: ACUMEN.
Hanning, G., Kretschmer, H., & Liu, Z. (2008). Distribution of co-author pairs ‘frequencies of the Journal of Information Technology. COLLNET Journal of Scientometrics and Information Management,2(1), 73–81.Google Scholar
Kretschmer, H. (2002). Similarities and dissimilarities in co-authorship networks; gestalt theory as explanation for well-ordered collaboration structures and production of scientific literature. Library Trends,50(3), 474–497.Google Scholar
Kretschmer, H., & Aguillo, I. F. (2004). Visibility of collaboration on the Web. Scientometrics,61(3), 405–426.CrossRefGoogle Scholar
Kretschmer, H., & Kretschmer, T. (2007). Lotka’s distribution and distribution of co-author Pairs’ frequencies. Journal of Informetrics,1, 308–337.CrossRefGoogle Scholar
Kretschmer, H., & Kretschmer, T. (2009). Invited keynote speech. Who is collaborating with whom? Explanation of a fundamental principle. In: H. Hou, B. Wang, S. Liu, Z. Hu, X. Zhang, M. Li (Eds.), Proceedings of the 5th International Conference on Webometrics, Informetrics and Scientometrics and 10th COLLNET Meeting, 13–16 September 2009, Dalian, China (CD-ROM for all participants and for libraries).Google Scholar
Kundra, R., Beaver, D., Kretschmer, H., & Kretschmer, T. (2008). Co- author pairs’ frequencies distribution in journals of gender studies. COLLNET Journal of Scientometrics and Information Management,2(1), 63–71.Google Scholar
Naldi, F., Parenti, I.V. (2002). Scientific and technological performance by gender: a feasibility study on patent and bibliometric indicators. Vol. II: methodological report. European Commission Research, EUR 20309.Google Scholar
Naldi, F., Luzi, D., Valente, A., & Parenti, I. V. (2004). Scientific and technological performance by gender. In H. F. Moed, et al. (Eds.), Handbook of quantitative science and technology research (pp. 299–314). The Netherlands: Kluwer Academic Publishers.Google Scholar
Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters,89, 208701.Google Scholar
Newman, M. E. J. (2005). Power laws, pareto distributions and Zipf’s law. Contemporary Physics,46(5), 323–351.CrossRefGoogle Scholar
Otte, E., & Rousseau, R. (2002). Social network analysis: a powerful strategy, also for the information sciences. Journal of Information Science,28, 443–455.CrossRefGoogle Scholar
Pepe, A., & Marko, A. R. (2009). Collaboration in sensor network research: an in-depth longitudinal analysis of assortative mixing patterns. Scientometrics,84(3), 687–701. This article is published with open access at Springerlink.com.CrossRefGoogle Scholar
Price, D. de Solla (1963). Little science, big science. New York: Columbia University Press.Google Scholar
Wasserman, S., & Faust, K. (1994). Social network analysis. Methods and applications (p. 1994). Cambridge: Cambridge University Press.CrossRefGoogle Scholar