A scaling between Impact Factor and uncitedness

The Impact Factor has become a well-known measure of the average citation number of articles published in a scientific journal. A journal with a high Impact Factor is assumed to have a low percentage of uncited articles. We show that the scaling relation between the Impact Factor and the uncited percentage can be understood by a simple mechanism. The empirical data can be reproduced by a random mechanism with the cumulative advantage. To further explore the robustness of such a mechanism, we investigate the relation between the average citation number and the uncited percentage from different perspectives. We apply the idea of Impact Factor to the publications of an institute in addition to its general application to the publications of a journal. We find that the same scaling relation can be obtained. We also show that a static relation can be applied to describe the time evolution of a dynamical process. These results provide further justification for the same citation mechanism behind different research fields.

[1]  Claudio Castellano,et al.  Universality of citation distributions: Toward an objective measure of scientific impact , 2008, Proceedings of the National Academy of Sciences.

[2]  Steve Pressé,et al.  Nonuniversal power law scaling in the probability distribution of scientific citations , 2010, Proceedings of the National Academy of Sciences.

[3]  Leo Egghe,et al.  The mathematical relation between the impact factor and the uncitedness factor , 2008, Scientometrics.

[4]  E. Garfield The history and meaning of the journal impact factor. , 2006, JAMA.

[5]  Ruben Coronel,et al.  Impact factors: no totum pro parte by skewness of citation. , 2004, Cardiovascular research.

[6]  Leo Egghe,et al.  Mathematical derivation of the impact factor distribution , 2009, J. Informetrics.

[7]  E. Garfield Citation indexes for science. A new dimension in documentation through association of ideas. 1955. , 1955, International journal of epidemiology.

[8]  H. Stanley,et al.  Methods for measuring the citations and productivity of scientists across time and discipline. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Quentin L. Burrell,et al.  A stochastic approach to the relation between the impact factor and the uncitedness factor , 2013, J. Informetrics.

[10]  R. Merton The Matthew Effect in Science , 1968, Science.

[11]  Ding-wei Huang,et al.  Dynamics of citation distribution , 2011, Comput. Phys. Commun..

[12]  R. Merton The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property , 1988, Isis.

[13]  Renio S. Mendes,et al.  Scaling behavior in the dynamics of citations to scientific journals , 2006 .

[14]  Kène Henkens,et al.  Demographers and Their Journals: Who Remains Uncited After Ten Years? , 2004 .

[15]  Thed N. van Leeuwen,et al.  Characteristics of journal impact factors: The effects of uncitedness and citation distribution on the understanding of journal impact factors , 2005, Scientometrics.

[16]  Ludo Waltman,et al.  Some Comments on Egghe’S Derivation of the Impact Factor Distribution , 2009, J. Informetrics.

[17]  Carl T. Bergstrom,et al.  Differences in impact factor across fields and over time , 2009 .

[18]  Woo-Sung Jung,et al.  Quantitative and empirical demonstration of the Matthew effect in a study of career longevity , 2008, Proceedings of the National Academy of Sciences.

[19]  M. Sales-Pardo,et al.  Effectiveness of Journal Ranking Schemes as a Tool for Locating Information , 2008, PloS one.

[20]  Lutz Bornmann,et al.  OPEN PEN ACCESS CCESS , 2008 .

[21]  Germinal Cocho,et al.  On the behavior of journal impact factor rank-order distribution , 2006, J. Informetrics.