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The Skewness of Science in 219 Sub-Fields and a Number of Aggregates

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  • Ruiz-Castillo, Javier
  • Ortuño-Ortin, Ignacio
  • Albarran, Pedro
  • Crespo, Juan A.

Abstract

This paper studies evidence from Thomson Scientific about the citation process of 3.7 million articles published in the period 1998-2002 in 219 Web of Science categories, or sub-fields. Reference and citation distributions have very different characteristics across sub-fields. However, when analyzed with the Characteristic Scores and Scales technique, which is size and scale independent, the shape of these distributions appear extraordinarily similar. Reference distributions are mildly skewed, but citation distributions with a five-year citation window are highly skewed: the mean is twenty points above the median, while 9-10% of all articles in the upper tail account for about 44% of all citations. The aggregation of sub-fields into disciplines and fields according to several aggregation schemes preserve this feature of citation distributions. On the other hand, for 140 of the 219 sub-fields the existence of a power law cannot be rejected. However, contrary to what is generally believed, at the sub-field level the scaling parameter is above 3.5 most of the time, and power laws are relatively small: on average, they represent 2% of all articles and account for 13.5% of all citations. The results of the aggregation into disciplines and fields reveal that power law algebra is a subtle phenomenon.

Suggested Citation

  • Ruiz-Castillo, Javier & Ortuño-Ortin, Ignacio & Albarran, Pedro & Crespo, Juan A., 2010. "The Skewness of Science in 219 Sub-Fields and a Number of Aggregates," CEPR Discussion Papers 8126, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:8126
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    1. S. Redner, 1998. "How popular is your paper? An empirical study of the citation distribution," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 4(2), pages 131-134, July.
    2. Pedro Albarrán & Javier Ruiz‐Castillo, 2011. "References made and citations received by scientific articles," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 40-49, January.
    3. Pedro Albarrán & Juan A. Crespo & Ignacio Ortuño & Javier Ruiz-Castillo, 2011. "The skewness of science in 219 sub-fields and a number of aggregates," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 385-397, August.
    4. Anthony F.J. van Raan, 2006. "Statistical properties of bibliometric indicators: Research group indicator distributions and correlations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 408-430, February.
    5. Irvine, John & Martin, Ben R., 1984. "CERN: Past performance and future prospects : II. The scientific performance of the CERN accelerators," Research Policy, Elsevier, vol. 13(5), pages 247-284, October.
    6. Wolfgang Glänzel & András Schubert, 2003. "A new classification scheme of science fields and subfields designed for scientometric evaluation purposes," Scientometrics, Springer;Akadémiai Kiadó, vol. 56(3), pages 357-367, March.
    7. Per O. Seglen, 1992. "The skewness of science," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 43(9), pages 628-638, October.
    8. Sune Lehmann & Andrew D. Jackson & Benny E. Lautrup, 2008. "A quantitative analysis of indicators of scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(2), pages 369-390, August.
    9. Schubert, András & Glänzel, Wolfgang, 2007. "A systematic analysis of Hirsch-type indices for journals," Journal of Informetrics, Elsevier, vol. 1(3), pages 179-184.
    10. Waltman, Ludo & van Eck, Nees Jan & Noyons, Ed C.M., 2010. "A unified approach to mapping and clustering of bibliometric networks," Journal of Informetrics, Elsevier, vol. 4(4), pages 629-635.
    11. Matthew O. Jackson & Brian W. Rogers, 2007. "Meeting Strangers and Friends of Friends: How Random Are Social Networks?," American Economic Review, American Economic Association, vol. 97(3), pages 890-915, June.
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    More about this item

    Keywords

    Citation analysis; Power laws; Research performance;
    All these keywords.

    JEL classification:

    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives
    • Y80 - Miscellaneous Categories - - Related Disciplines - - - Related Disciplines
    • Z00 - Other Special Topics - - General - - - General

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