论文标题
在研究组织绩效评估中，无监督的作者置态算法的可靠性如何？
How reliable are unsupervised author disambiguation algorithms in the assessment of research organization performance?
论文作者
论文摘要
本文研究了评估基于无监督的作者名称歧义算法时研究组织绩效排名的偏见程度。它使用Caron and Van Eck（2014）的无监督方法进行了研究表现评估练习的结果，以推导大学的研究人员，并使用D'Angelo，Giuffrida和Abramo（2011）的D'Angelo，Giuffrida（2011）的Bengorithm的基准测试。可以在国家或国际兴趣的其他框架中复制开发的方法进行比较分析，这意味着从业者可以精确地衡量使用无监督算法进行任何评估练习中固有的扭曲程度。反过来，这可能对政策制定者关于是否投资建立国家研究人员数据库的决定很有用，而不是通过其测量偏见来解决无监督的方法。
The paper examines extent of bias in the performance rankings of research organisations when the assessments are based on unsupervised author-name disambiguation algorithms. It compares the outcomes of a research performance evaluation exercise of Italian universities using the unsupervised approach by Caron and van Eck (2014) for derivation of the universities' research staff, with those of a benchmark using the supervised algorithm of D'Angelo, Giuffrida, and Abramo (2011), which avails of input data. The methodology developed could be replicated for comparative analyses in other frameworks of national or international interest, meaning that practitioners would have a precise measure of the extent of distortions inherent in any evaluation exercises using unsupervised algorithms. This could in turn be useful in informing policy-makers' decisions on whether to invest in building national research staff databases, instead of settling for the unsupervised approaches with their measurement biases.
