论文标题
大鱼和小池塘：为什么不应使用部门H-Index来排名大学
Big fish and small ponds: why the departmental h-index should not be used to rank universities
论文作者
论文摘要
本文重新考虑了所谓的组或部门H指数的大小依赖性。尽管单位规模对此类集体措施的影响已经证明了十年前，但仍可以找到基于该指标的机构评级，并且仍会影响许多研究机构的声誉和资金。本文的目的是以一种简单的方式证明这种集体研究质量评估的方法的谬误，重点是以其原始形式的H索引。我们表明，在不同规模的机构中，随机重新安装真实的科学计量数据（引用数量不同），同时保持其研究输出的数量，对其部门的H索引影响很小。这表明基于集体H指数的评级中的相对位置不仅取决于特定研究产出的质量（影响），而且还取决于其体积。因此，在其原始形式中，集体h-Index的应用是在整个级别（例如研究小组，机构或期刊）进行比较的地下室。我们为这种失败提出了一种可能的补救措施，该方法可以以与H-Index本身一样简单易懂的方式实现。
The size-dependent nature of the so-called group or departmental h-index is reconsidered in this paper. While the influence of unit size on such collective measures was already demonstrated a decade ago, institutional ratings based on this metric can still be found and still impact on the reputations and funding of many research institutions. The aim of this paper is to demonstrate the fallacy of this approach to collective research-quality assessment in a simple way, focusing on the h-index in its original form. We show that randomly reshuffling real scientometric data (varying numbers of citations) amongst institutions of varying size, while maintaining the volume of their research outputs, has little effect on their departmental h-index. This suggests that the relative position in ratings based on the collective h-index is determined not only by quality (impact) of particular research outputs but by their volume. Therefore, the application of the collective h-index in its original form is disputable as a basement for comparison at aggregated levels such as to research groups, institutions or journals. We suggest a possible remedy for this failing which is implementable in a manner that is as simple and understandable as the h-index itself.
