The UK Research Excellence Framework and the Matthew effect: Insights from machine learning.
The UK Research Excellence Framework and the Matthew effect: Insights from machine learning.
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DOI：
10.1371/journal.pone.0207919
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发表时间：
2018
期刊：
影响因子：
3.7
通讯作者：
Balbuena LD
中科院分区：
文献类型：
--
作者：
Balbuena LD
由于英国的研究评估工作成本高昂，许多人呼吁采用更简单、耗时更少的替代方案。在这项工作中，我们收集了公开可用的REF数据，将其与图书馆订阅的数据相结合，并使用机器学习来检查2014年卓越研究框架的总体结果是否可以复制。本文建立了一个贝叶斯加性回归树模型，从18个候选解释变量中预测大学平均成绩。109所大学被随机分为训练集（n = 79）和测试集（n = 30）。该模型“学习”了GPA与训练集中其他变量之间的关联，并预测了测试集中大学的GPA。GPA可以通过三个变量来预测：Web of Science文档的数量，入学关税和来自公立学校的学生百分比（r平方= 0.88）。这一发现的影响进行了讨论，并提出了建议。
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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影响因子：
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通讯作者：
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DOI：
10.1002/asi.20806
发表时间：
2008-03-01
影响因子：
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作者：
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通讯作者：
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