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Open access scientometrics and the UK Research Assessment Exercise

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  • Stevan Harnad

    (Université du Québec à Montréal)

Abstract

Scientometric predictors of research performance need to be validated by showing that they have a high correlation with the external criterion they are trying to predict. The UK Research Assessment Exercise (RAE) — together with the growing movement toward making the full-texts of research articles freely available on the web — offer a unique opportunity to test and validate a wealth of old and new scientometric predictors, through multiple regression analysis: Publications, journal impact factors, citations, co-citations, citation chronometrics (age, growth, latency to peak, decay rate), hub/authority scores, h-index, prior funding, student counts, co-authorship scores, endogamy/exogamy, textual proximity, download/co-downloads and their chronometrics, etc. can all be tested and validated jointly, discipline by discipline, against their RAE panel rankings in the forthcoming parallel panel-based and metric RAE in 2008. The weights of each predictor can be calibrated to maximize the joint correlation with the rankings. Open Access Scientometrics will provide powerful new means of navigating, evaluating, predicting and analyzing the growing Open Access database, as well as powerful incentives for making it grow faster.

Suggested Citation

  • Stevan Harnad, 2009. "Open access scientometrics and the UK Research Assessment Exercise," Scientometrics, Springer;Akadémiai Kiadó, vol. 79(1), pages 147-156, April.
    • Handle: RePEc:spr:scient:v:79:y:2009:i:1:d:10.1007_s11192-009-0409-z
    DOI: 10.1007/s11192-009-0409-z
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    References listed on IDEAS

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    1. Tim Brody & Stevan Harnad & Leslie Carr, 2006. "Earlier Web usage statistics as predictors of later citation impact," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(8), pages 1060-1072, June.
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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. Sven E. Hug & Mirjam Aeschbach, 2020. "Criteria for assessing grant applications: a systematic review," Palgrave Communications, Palgrave Macmillan, vol. 6(1), pages 1-15, December.
    3. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    4. Wumei Du & Zheng Xie & Yiqin Lv, 2021. "Predicting publication productivity for authors: Shallow or deep architecture?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5855-5879, July.
    5. Marcin Kozak & Lutz Bornmann, 2012. "A New Family of Cumulative Indexes for Measuring Scientific Performance," PLOS ONE, Public Library of Science, vol. 7(10), pages 1-4, October.
    6. 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.
    7. Brito, Ricardo & Navarro, Alonso Rodríguez, 2021. "The inconsistency of h-index: A mathematical analysis," Journal of Informetrics, Elsevier, vol. 15(1).
    8. Shahd Al-Janabi & Lee Wei Lim & Luca Aquili, 2021. "Development of a tool to accurately predict UK REF funding allocation," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 8049-8062, September.
    9. Rongying Zhao & Xu Wang, 2019. "Evaluation and comparison of influence in international Open Access journals between China and USA," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(3), pages 1091-1110, September.

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