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Differences in citation impact across countries

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  • Pedro Albarrán
  • Antonio Perianes-Rodríguez
  • Javier Ruiz-Castillo

Abstract

Using a large dataset, indexed by Thomson Reuters, consisting of 4.4 million articles published in 1998-2003 with a five-year citation window for each year, this paper studies country citation distributions in a partition of the world into 36 countries and two geographical areas in the all-sciences case and eight broad scientific fields. The key findings are the following two. Firstly, the shape of country citation distributions is highly skewed and very similar to each other across all fields. Secondly, differences in country citation distributions appear to have a strong scale factor component. The implication is that, in spite of the skewness of citation distributions, international comparisons of citation impact in terms of country mean citations capture well such scale factors. The empirical scenario described in the paper helps understanding why, in each field and the all-sciences case, the country rankings according to (i) mean citations and (ii) the percentage of articles in each country belonging to the set formed by the 10% of the more highly cited papers are so similar to each other.
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Suggested Citation

  • Pedro Albarrán & Antonio Perianes-Rodríguez & Javier Ruiz-Castillo, 2015. "Differences in citation impact across countries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(3), pages 512-525, March.
    • Handle: RePEc:bla:jinfst:v:66:y:2015:i:3:p:512-525
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    1. 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.
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    Cited by:

    1. Rodríguez-Navarro, Alonso & Brito, Ricardo, 2018. "Double rank analysis for research assessment," Journal of Informetrics, Elsevier, vol. 12(1), pages 31-41.
    2. Peter Vinkler, 2018. "Structure of the scientific research and science policy," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 737-756, February.
    3. Albarrán, Pedro & Herrero, Carmen & Ruiz-Castillo, Javier & Villar, Antonio, 2017. "The Herrero-Villar approach to citation impact," Journal of Informetrics, Elsevier, vol. 11(2), pages 625-640.
    4. Philip Shapira & Seokbeom Kwon & Jan Youtie, 2017. "Tracking the emergence of synthetic biology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1439-1469, September.
    5. Alonso Rodríguez-Navarro & Ricardo Brito, 2019. "Probability and expected frequency of breakthroughs: basis and use of a robust method of research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 213-235, April.
    6. Fairclough, Ruth & Thelwall, Mike, 2015. "More precise methods for national research citation impact comparisons," Journal of Informetrics, Elsevier, vol. 9(4), pages 895-906.
    7. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    8. Thelwall, Mike, 2016. "The precision of the arithmetic mean, geometric mean and percentiles for citation data: An experimental simulation modelling approach," Journal of Informetrics, Elsevier, vol. 10(1), pages 110-123.
    9. Thelwall, Mike, 2016. "Are the discretised lognormal and hooked power law distributions plausible for citation data?," Journal of Informetrics, Elsevier, vol. 10(2), pages 454-470.
    10. Thelwall, Mike & Fairclough, Ruth, 2017. "The accuracy of confidence intervals for field normalised indicators," Journal of Informetrics, Elsevier, vol. 11(2), pages 530-540.
    11. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.

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