Tijssen Robert J W, Yegros-Yegros Alfredo, Winnink Jos J
Centre for Science and Technology Studies (CWTS), Leiden University, PO Box 905, 2300 AX Leiden, The Netherlands ; DST-NRF Center of Excellence in Scientometrics and Science, Technology and Innovation Policy, Stellenbosch University, Stellenbosch, South Africa.
Centre for Science and Technology Studies (CWTS), Leiden University, PO Box 905, 2300 AX Leiden, The Netherlands.
Scientometrics. 2016;109(2):677-696. doi: 10.1007/s11192-016-2098-8. Epub 2016 Aug 13.
In September 2015 Thomson Reuters published its (RIU). Covering 100 large research-intensive universities worldwide, Stanford University came in first, MIT was second and Harvard in third position. But how meaningful is this outcome? In this paper we will take a critical view from a methodological perspective. We focus our attention on the various types of metrics available, whether or not data redundancies are addressed, and if metrics should be assembled into a single composite overall score or not. We address these issues in some detail by emphasizing one metric in particular: university-industry co-authored publications (UICs). We compare the RIU with three variants of our own -, which we derived from the bibliometric analysis of 750 research universities worldwide. Our findings highlight conceptual and methodological problems with UIC-based data, as well as computational weaknesses such university ranking systems. Avoiding choices between size-dependent or independent metrics, and between single-metrics and multi-metrics systems, we recommend an alternative 'scoreboard' approach: (1) without weighing systems of metrics and composite scores; (2) computational procedures and information sources are made more transparent; (3) size-dependent metrics are kept separate from size-independent metrics; (4) UIC metrics are selected according to the type of proximity relationship between universities and industry.
2015年9月,汤森路透发布了其《全球最具创新力大学报告》(RIU)。该报告涵盖了全球100所大型研究密集型大学,斯坦福大学位居榜首,麻省理工学院位列第二,哈佛大学排名第三。但这一结果有多大意义呢?在本文中,我们将从方法论的角度进行批判性审视。我们关注现有的各类指标、是否解决了数据冗余问题,以及指标是否应汇总为单一的综合总分。我们通过特别强调一个指标——大学与产业合作发表的论文(UICs),来详细探讨这些问题。我们将RIU与我们自己的三个变体进行比较,这三个变体是我们通过对全球750所研究型大学的文献计量分析得出的。我们的研究结果凸显了基于UIC数据的概念和方法问题,以及此类大学排名系统的计算缺陷。为避免在依赖规模或不依赖规模的指标之间,以及单一指标和多指标系统之间做出选择,我们推荐一种替代性的“记分牌”方法:(1)不权衡指标系统和综合得分;(2)使计算程序和信息来源更加透明;(3)将依赖规模的指标与不依赖规模的指标分开;(4)根据大学与产业之间的接近关系类型选择UIC指标。