Advertisement

Scientometrics

, Volume 109, Issue 3, pp 1711–1724 | Cite as

The dispersion of the citation distribution of top scientists’ publications

  • Giovanni Abramo
  • Ciriaco Andrea D’Angelo
  • Anastasiia Soldatenkova
Article

Abstract

This work explores the distribution of citations for the publications of top scientists. A first objective is to find out whether the 80–20 Pareto rule applies, that is if 80 % of the citations to a top scientist’s work concern 20 % of their publications. Observing that the rule does not apply, we also measure the dispersion of the citation distribution by means of the Gini coefficient. Further, we investigate the question of what share of a top scientist’ publications go uncited. Finally, we study the relation between the dispersion of the citation distribution and the share of uncited publications. As well as the overall level, the analyses are carried out at the field and discipline level, to assess differences across them.

Keywords

Bibliometrics Research evaluation Italy University Pareto Gini 

References

  1. Abramo, G., Cicero, T., & D’Angelo, C. A. (2011). The dangers of performance-based research funding in non-competitive higher education systems. Scientometrics, 87(3), 641–654.CrossRefGoogle Scholar
  2. Abramo, G., Cicero, T., & D’Angelo, C. A. (2012). Revisiting the scaling of citations for research assessment. Journal of Informetrics, 6(4), 470–479.CrossRefGoogle Scholar
  3. Abramo, G., & D’Angelo, C. A. (2011). National-scale research performance assessment at the individual level. Scientometrics, 86(2), 347–364.CrossRefGoogle Scholar
  4. Abramo, G., D’Angelo, C. A., & Di Costa, F. (2010). Testing the trade-off between productivity and quality in research activities. Journal of the American Society for Information Science and Technology, 61(1), 132–140.CrossRefGoogle Scholar
  5. Bosquet, C., & Combes, P. P. (2013). Are academics who publish more also more cited? Individual determinants of publication and citation records. Scientometrics, 97(3), 831–857.CrossRefGoogle Scholar
  6. D’Angelo, C. A., Giuffrida, C., & Abramo, G. (2011). A heuristic approach to author name disambiguation in bibliometrics databases for large-scale research assessments. Journal of the American Society for Information Science and Technology, 62(2), 257–269.CrossRefGoogle Scholar
  7. Flegl M., & Vydrova H. V. (2014). Is Pareto’s 50-20 rule applicable in research? A case of Culs Prague. In 11th conference on “efficiency and responsibility in education”, Prague, June 2014.Google Scholar
  8. Glanzel, W., & Moed, H. F. (2002). Journal impact measures in bibliometric research. Scientometrics, 53(2), 171–193.CrossRefGoogle Scholar
  9. Gupta, H. M., Campanha, J. R., & Pesce, R. A. G. (2005). Power-law distributions for the citation index of scientific publications and scientists. Brazilian Journal of Physics, 35(4A), 981–986.CrossRefGoogle Scholar
  10. Moed, H. F., & Van Leeuwen, Th N. (1996). Impact factors can mislead. Nature, 381, 186.CrossRefGoogle Scholar
  11. Parker, J. N., Allesina, S., & Lortie, C. J. (2013). Characterizing a scientific elite (B): publication and citation patterns of the most highly cited scientists in environmental science and ecology. Scientometrics, 94(2), 469–480.CrossRefGoogle Scholar
  12. Perianes-Rodriguez, A., & Ruiz-Castillo, J. (2015). University citation distributions. Journal of the American Society for Information Science and Technology. doi: 10.1002/asi.23619.Google Scholar
  13. Piro, F. N., Rørstad, K., & Aksnes, D. W. (2016). How does prolific professors influence on the citation impact of their university departments? Scientometrics, 107(3), 941–961.CrossRefGoogle Scholar
  14. Ruiz-Castillo, J., & Costas, R. (2014). The skewness of scientific productivity. Journal of Informetrics, 8(4), 917–934.CrossRefGoogle Scholar
  15. Seglen, P. O. (1997). Why the impact factor of journals should not be used for evaluating research. British Medical Journal, 314(7079), 497–502.CrossRefGoogle Scholar
  16. Weingart, P. (2004). Impact of bibliometrics upon the science system: inadvertent consequences? In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook on quantitative science and technology research. Dordrecht (The Netherlands): Kluwer.Google Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  • Giovanni Abramo
    • 1
  • Ciriaco Andrea D’Angelo
    • 1
    • 2
  • Anastasiia Soldatenkova
    • 2
  1. 1.Laboratory for Studies of Research Evaluation Institute for System Analysis and Computer Science (IASI-CNR)National Research Council of ItalyRomeItaly
  2. 2.Department of Engineering and ManagementUniversity of Rome ‘Tor Vergata’RomeItaly

Personalised recommendations