Skip to main content
Log in

Identifying the global core-periphery structure of science

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

While there is a consensus that there is a core-periphery structure in the global scientific enterprise, there have not been many methodologies developed for identifying this structure. This paper develops a methodology by looking at the differences in the power law structure of article outputs and degree centrality distributions of countries. This methodology is applied to five different scientific fields: astronomy and astrophysics, energy and fuels, nanotechnology and nanosciences, nutrition, and oceanography. This methodology uncovers a two-tiered power law structure that exists in all examined fields. The core-periphery structure that is unique to each field is characterized by the core’s size, minimum degree, and exponent of its power law distribution. Stark differences are identified between technology and non-technology intensive scientific fields.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. England, Wales, and Scotland are treated as separate entities in WoS and were combined into the United Kingdom. Additionally, East and West Germany were combined into a single entity prior to 1989.

  2. The cause for this discrepancy was not investigated. However, the author hypothesizes that part of this discrepancy may come from the fact that WoS includes a large number of professional journals.

References

  • Amaral, L. A. N., & Ottino, J. M. (2004). Complex networks: Augmenting the framework for the study of complex systems. European Physical Journal B, 38, 147–162.

    Article  Google Scholar 

  • Barabasi, A. L., Jeong, H., Neda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A, 311, 590–614.

    Article  MathSciNet  MATH  Google Scholar 

  • Ben-David, J. (1971). The scientist’s role in society. NJ: Prentice-Hall.

    Google Scholar 

  • Borgatti, S. P., & Everett, M. G. (1999). Models of core/periphery structures. Social Networks, 21, 375–395.

    Article  Google Scholar 

  • Cioffi-Revilla, C. (Eds.). Power laws in the social sciences: Discovering complexity and non-equilibrium dynamics in the social universe. Publication forthcoming.

  • De Solla, D. J. (1965). Networks of scientific papers. Science, 149(3683), 510–515.

    Article  Google Scholar 

  • Georghiou, L. (1998). Global cooperation in research. Research Policy, 27, 611–626.

    Article  Google Scholar 

  • Glänzel, W. (2001). National characteristics in international scientific co-authorship relations. Scientometrics, 51(1), 69–115.

    Article  Google Scholar 

  • Glänzel, W., & Schubert, A. (2004). Analyzing scientific networks through co-authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research. The use of publication and patent statistis in studies of S&T systems (pp. 257–276). Dordrecht: Kluwer Academic Press.

    Google Scholar 

  • Glänzel, W., Debackere, K., & Mayer, M. (2008). ‘Triad’ or ‘tetrad’? On global changes in a dynamic world. Scientometrics, 74(1), 71–88.

    Article  Google Scholar 

  • Hill, C.T. (2007). The post-scientific society. Issues in Science and Technology, 24(1).

  • Holland, J. (1998). Emergence: From chaos to order. New York: Helix Books.

    MATH  Google Scholar 

  • Hwang, K. (2008). International collaboration in multilayered center-periphery in the globalization of science and technology. Science Technology Human Values, 33, 101–133.

    Google Scholar 

  • Jeong, H., Neda, A., & Barabasi, A.L. (2001). Measuring preferential attachment for evolving networks. arXiv:cond-mat/0104131 v1, 7 April 2001.

  • Katz, J. S., & Hicks, D. (1997). How much is a collaboration worth? A calibrated bibliometric model. Scientometrics, 40(3), 541–554.

    Article  Google Scholar 

  • King, D.A. (2004). The scientific impact of nations. Nature, 432.

  • Lall, S. (2001). Competitiveness indices and developing countries: An economic evaluation of the global competitiveness report. World Development, 29(9), 1501–1525.

    Article  Google Scholar 

  • Leydesdorff, L., & Wagner, C. S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317–325.

    Article  Google Scholar 

  • Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–324.

    Google Scholar 

  • Newman, M. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98, 404–409.

    Article  MATH  Google Scholar 

  • Newman, M. E. J. (2004). Co-authorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(Suppl. 1), 5200–5205.

    Article  Google Scholar 

  • Ohmae, K. (1985). Triad Power: The Coming Shape of Global Competition. New York: Free Press.

  • Oldham, G. (2005). Policy brief: International scientific collaboration: a quick guide, science and development network.

  • Schott, T. (1993). World science: Globalization of institutions and participation. Science, Technology and Human Values, 18, 196–208.

    Article  Google Scholar 

  • Schott, T. (1998). Ties between center and periphery in the scientific world system. Journal of World-Systems Research, 4, 112–144.

    Google Scholar 

  • Serrano, M. A. (2008). Rich-club vs. rich-multipolarization phenomena in weighted networks. Physical Review E, 78, 026101.

    Article  Google Scholar 

  • Traweek, S. (1988). Beamtimes and lifetimes: The world of high energy physicists. Cambridge, MA: Harvard University Press.

  • Wagner, C. S. (2005). Six case studies of international collaboration in science. Scientometrics, 62(1), 3–26.

    Article  Google Scholar 

  • Wagner, C. S. (2008). The new invisible college: Science for development. Washington DC: Brookings Institution Press.

    Google Scholar 

  • Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34(10), 1608–1618.

    Article  Google Scholar 

  • Wagner-Döbler, R. (2001). Continuity and discontinuity of collaboration behaviour since 1800—from a bibliometric point of view. Scientometrics, 52, 503–517.

    Article  Google Scholar 

  • Zelnio, R. (2011). Structure of international scientific collaboration. In E. Noyons, P. Ngulube, J. Leta (Eds), Proceedings of ISSI 2011—the 13th International Conference on Scientometrics and Informetrics, Durban, South Africa (pp. 898–913).

  • Zhou, S., & Mondragón, R. J. (2004). The rich-club phenomenon in the internet topology. IEEE Communication Letters, 8(3), 180–181.

    Article  Google Scholar 

Download references

Acknowledgments

An extended version of a paper presented at the 13th International Conference on Scientometrics and Informetrics, Durban (South Africa), 4–7 July 2011 (Zelnio 2011).

The author would like to thank the conference attendees and the anonymous conference reviewers for their comments, many of which were incorporated.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ryan Zelnio.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Zelnio, R. Identifying the global core-periphery structure of science. Scientometrics 91, 601–615 (2012). https://doi.org/10.1007/s11192-011-0598-0

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-011-0598-0

Keywords

Navigation