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Quality versus quantity in scientific impact


  • Kaur, Jasleen
  • Ferrara, Emilio
  • Menczer, Filippo
  • Flammini, Alessandro
  • Radicchi, Filippo


Citation metrics are becoming pervasive in the quantitative evaluation of scholars, journals, and institutions. Hiring, promotion, and funding decisions increasingly rely on a variety of impact metrics that cannot disentangle quality from quantity of scientific output, and are biased by factors such as discipline and academic age. Biases affecting the evaluation of single papers are compounded when one aggregates citation-based metrics across an entire publication record. It is not trivial to compare the quality of two scholars that during their careers have published at different rates, in different disciplines, and in different periods of time. Here we evaluate a method based on the generation of a statistical baseline specifically tailored on the academic profile of each researcher. We demonstrate the effectiveness of the approach in decoupling the roles of quantity and quality of publications to explain how a certain level of impact is achieved. The method can be extended to simultaneously suppress any source of bias. As an illustration, we use it to capture the quality of the work of Nobel laureates irrespective of number of publications, academic age, and discipline, even when traditional metrics indicate low impact in absolute terms. The procedure is flexible enough to allow for the evaluation of, and fair comparison among, arbitrary collections of papers – scholar publication records, journals, and institutions; in fact, it extends a similar technique that was previously applied to the ranking of research units and countries in specific disciplines (Crespo, Ortuño-Ortí, & Ruiz-Castillo, 2012). We further apply the methodology to almost a million scholars and over six thousand journals to measure the impact that cannot be explained by the volume of publications alone.

Suggested Citation

  • Kaur, Jasleen & Ferrara, Emilio & Menczer, Filippo & Flammini, Alessandro & Radicchi, Filippo, 2015. "Quality versus quantity in scientific impact," Journal of Informetrics, Elsevier, vol. 9(4), pages 800-808.
  • Handle: RePEc:eee:infome:v:9:y:2015:i:4:p:800-808
    DOI: 10.1016/j.joi.2015.07.008

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    References listed on IDEAS

    1. Davis, Paul & Papanek, Gustav F, 1984. "Faculty Ratings of Major Economics Departments by Citations," American Economic Review, American Economic Association, vol. 74(1), pages 225-230, March.
    2. 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.
    3. Ruiz-Castillo, Javier & Costas, Rodrigo, 2014. "The skewness of scientific productivity," Journal of Informetrics, Elsevier, vol. 8(4), pages 917-934.
    4. Waltman, Ludo & van Eck, Nees Jan & van Leeuwen, Thed N. & Visser, Martijn S. & van Raan, Anthony F.J., 2011. "Towards a new crown indicator: Some theoretical considerations," Journal of Informetrics, Elsevier, vol. 5(1), pages 37-47.
    5. Kaur, Jasleen & Radicchi, Filippo & Menczer, Filippo, 2013. "Universality of scholarly impact metrics," Journal of Informetrics, Elsevier, vol. 7(4), pages 924-932.
    6. Ruiz-Castillo, Javier & Ortuño, Ignacio & Crespo, Juan A., 2012. "The citation merit of scientific publications," UC3M Working papers. Economics we1136, Universidad Carlos III de Madrid. Departamento de Economía.
    7. Ruiz-Castillo, Javier & Albarrán, Pedro, 2012. "The measurement of scientific excellence around the world," UC3M Working papers. Economics we1208, Universidad Carlos III de Madrid. Departamento de Economía.
    8. Loet Leydesdorff & Lutz Bornmann & Rüdiger Mutz & Tobias Opthof, 2011. "Turning the tables on citation analysis one more time: Principles for comparing sets of documents," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(7), pages 1370-1381, July.
    9. Alonso, S. & Cabrerizo, F.J. & Herrera-Viedma, E. & Herrera, F., 2009. "h-Index: A review focused in its variants, computation and standardization for different scientific fields," Journal of Informetrics, Elsevier, vol. 3(4), pages 273-289.
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    Cited by:

    1. Amjad, Tehmina & Ding, Ying & Xu, Jian & Zhang, Chenwei & Daud, Ali & Tang, Jie & Song, Min, 2017. "Standing on the shoulders of giants," Journal of Informetrics, Elsevier, vol. 11(1), pages 307-323.
    2. repec:spr:scient:v:111:y:2017:i:3:d:10.1007_s11192-017-2374-2 is not listed on IDEAS
    3. Mariani, Manuel Sebastian & Medo, Matúš & Zhang, Yi-Cheng, 2016. "Identification of milestone papers through time-balanced network centrality," Journal of Informetrics, Elsevier, vol. 10(4), pages 1207-1223.
    4. Lina M. Cortés & Andrés Mora-Valencia & Javier Perote, 2016. "The productivity of top researchers: a semi-nonparametric approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 891-915, November.
    5. Bouyssou, Denis & Marchant, Thierry, 2016. "Ranking authors using fractional counting of citations: An axiomatic approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 183-199.


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