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The UK Research Excellence Framework and the Matthew effect: Insights from machine learning

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  • Lloyd D Balbuena

Abstract

With the high cost of the research assessment exercises in the UK, many have called for simpler and less time-consuming alternatives. In this work, we gathered publicly available REF data, combined them with library-subscribed data, and used machine learning to examine whether the overall result of the Research Excellence Framework 2014 could be replicated. A Bayesian additive regression tree model predicting university grade point average (GPA) from an initial set of 18 candidate explanatory variables was developed. One hundred and nine universities were randomly divided into a training set (n = 79) and test set (n = 30). The model “learned” associations between GPA and the other variables in the training set and was made to predict the GPA of universities in the test set. GPA could be predicted from just three variables: the number of Web of Science documents, entry tariff, and percentage of students coming from state schools (r-squared = .88). Implications of this finding are discussed and proposals are given.

Suggested Citation

  • Lloyd D Balbuena, 2018. "The UK Research Excellence Framework and the Matthew effect: Insights from machine learning," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-13, November.
    • Handle: RePEc:plo:pone00:0207919
    DOI: 10.1371/journal.pone.0207919
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    References listed on IDEAS

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    1. Auranen, Otto & Nieminen, Mika, 2010. "University research funding and publication performance--An international comparison," Research Policy, Elsevier, vol. 39(6), pages 822-834, July.
    2. O. Mryglod & R. Kenna & Yu. Holovatch & B. Berche, 2015. "Predicting results of the research excellence framework using departmental h-index: revisited," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(3), pages 1013-1017, September.
    3. O. Mryglod & R. Kenna & Yu. Holovatch & B. Berche, 2015. "Predicting results of the Research Excellence Framework using departmental h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2165-2180, March.
    4. Ben R Martin, 2011. "The Research Excellence Framework and the ‘impact agenda’: are we creating a Frankenstein monster?," Research Evaluation, Oxford University Press, vol. 20(3), pages 247-254, September.
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    1. 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.

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