May 20, 2022

New Research Article: “The UK Research Excellence Framework and the Matthew Effect: Insights From Machine Learning”

The following article was published on Monday, November 26, 2018 by PLoS One.


The UK Research Excellence Framework and the Matthew Effect: Insights From Machine Learning


Lloyd D. Balbuena
University of Saskatchewan


PLoS ONE 13(11): e0207919


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.

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Gary Price ( is a librarian, writer, consultant, and frequent conference speaker based in the Washington D.C. metro area. Before launching INFOdocket, Price and Shirl Kennedy were the founders and senior editors at ResourceShelf and DocuTicker for 10 years. From 2006-2009 he was Director of Online Information Services at, and is currently a contributing editor at Search Engine Land.