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Evaluating universities using simple scientometric research-output metrics: Total citation counts per university for a retrospective seven-year rolling sample

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  • Bruce G Charlton
  • Peter Andras

Abstract

We advocate a scientometric, top-down and institution-based research-assessment methodology that is based on total citations accumulated from all publications associated with a specific university during the survey period. The exercise could be done every year using a rolling seven-year retrospective sample and should be performed by at least two independent auditors. Identification of elite ‘revolutionary-science’ institutions could be accomplished using a metric derived from the distribution of science Nobel Prizes. Copyright , Beech Tree Publishing.

Suggested Citation

  • Bruce G Charlton & Peter Andras, 2007. "Evaluating universities using simple scientometric research-output metrics: Total citation counts per university for a retrospective seven-year rolling sample," Science and Public Policy, Oxford University Press, vol. 34(8), pages 555-563, October.
  • Handle: RePEc:oup:scippl:v:34:y:2007:i:8:p:555-563
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    File URL: http://hdl.handle.net/10.3152/030234207X254413
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    Citations

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    Cited by:

    1. Goodall, Amanda H., 2009. "Highly cited leaders and the performance of research universities," Research Policy, Elsevier, vol. 38(7), pages 1079-1092, September.
    2. Hyeonchae Yang & Woo-Sung Jung, 2015. "A strategic management approach for Korean public research institutes based on bibliometric investigation," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(4), pages 1437-1464, July.
    3. Wai Ching Poon & Gareth D. Leeves, 2017. "Is there gender gap unequivocally? Evidence from research output 1958–2008," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1687-1701, June.
    4. Liang Zhang & Wei Bao & Liang Sun, 2016. "Resources and Research Production in Higher Education: A Longitudinal Analysis of Chinese Universities, 2000–2010," Research in Higher Education, Springer;Association for Institutional Research, vol. 57(7), pages 869-891, November.
    5. José Manuel Pastor & Lorenzo Serrano & Irene Zaera, 2015. "The research output of European higher education institutions," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1867-1893, March.
    6. Jane Payumo & Taurean Sutton & Derek Brown & Dan Nordquist & Marc Evans & Danna Moore & Prema Arasu, 2017. "Input–output analysis of international research collaborations: a case study of five U.S. universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1657-1671, June.
    7. João Carlos Nabout & Fabrício Barreto Teresa & Karine Borges Machado & Vitor Hugo Mendonça Prado & Luis Mauricio Bini & José Alexandre Felizola Diniz-Filho, 2018. "Do traditional scientometric indicators predict social media activity on scientific knowledge? An analysis of the ecological literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1007-1015, May.
    8. Xu, Shuqi & Mariani, Manuel Sebastian & Lü, Linyuan & Medo, Matúš, 2020. "Unbiased evaluation of ranking metrics reveals consistent performance in science and technology citation data," Journal of Informetrics, Elsevier, vol. 14(1).
    9. Hamid Bouabid & Mohamed Dalimi & Zayer ElMajid, 2011. "Impact evaluation of the voluntary early retirement policy on research and technology outputs of the faculties of science in Morocco," Scientometrics, Springer;Akadémiai Kiadó, vol. 86(1), pages 125-132, January.
    10. Thelwall, Mike, 2016. "The discretised lognormal and hooked power law distributions for complete citation data: Best options for modelling and regression," Journal of Informetrics, Elsevier, vol. 10(2), pages 336-346.

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