Mingers John, O'Hanley Jesse R, Okunola Musbaudeen
Kent Business School, University of Kent, Canterbury, CT2 7FS UK.
Scientometrics. 2017;113(3):1627-1643. doi: 10.1007/s11192-017-2532-6. Epub 2017 Oct 3.
In recent years, the extent of formal research evaluation, at all levels from the individual to the multiversity has increased dramatically. At the institutional level, there are world university rankings based on an ad hoc combination of different indicators. There are also national exercises, such as those in the UK and Australia that evaluate research outputs and environment through peer review panels. These are extremely costly and time consuming. This paper evaluates the possibility of using Google Scholar (GS) institutional level data to evaluate university research in a relatively automatic way. Several citation-based metrics are collected from GS for all 130 UK universities. These are used to evaluate performance and produce university rankings which are then compared with various rankings based on the 2014 UK Research Excellence Framework (REF). The rankings are shown to be credible and to avoid some of the obvious problems of the REF ranking, as well as being highly efficient and cost effective. We also investigate the possibility of normalizing the results for the university subject mix since science subjects generally produce significantly more citations than social science or humanities.
近年来，从个人到多所大学的各级正式研究评估的范围都急剧增加。在机构层面，有基于不同指标的临时组合的世界大学排名。也有国家层面的评估活动，比如英国和澳大利亚通过同行评审小组来评估研究成果和环境的活动。这些活动成本极高且耗时极长。本文评估了使用谷歌学术（GS）机构层面数据以相对自动化的方式评估大学研究的可能性。从GS收集了英国所有130所大学基于引用的多项指标。这些指标被用于评估绩效并生成大学排名，然后将这些排名与基于2014年英国卓越研究框架（REF）的各种排名进行比较。结果表明这些排名是可信的，并且避免了REF排名的一些明显问题，同时还具有高效性和成本效益。我们还研究了针对大学学科组合对结果进行归一化处理的可能性，因为理科科目通常比社会科学或人文学科产生的引用显著更多。
