全球智库信息集成服务系统(G2TT): DP7886 High- and Low-Impact Citation Measures: Empirical Applications

G2TT

来源类型	Discussion paper
规范类型	论文
来源ID	DP7886
	DP7886 High- and Low-Impact Citation Measures: Empirical Applications
	Javier Ruiz-Castillo; Ignacio Ortuño-Ortin; Pedro Albarran
发表日期	2010-06-01
出版年	2010
语种	英语
摘要	This paper contains the first empirical applications of a novel methodology for comparing the citation distributions of research units working in the same homogeneous field. The paper considers a situation in which the world citation distribution in 22 scientific fields is partitioned into three geographical areas: the U.S., the European Union (EU ), and the rest of the world (RW ). Given a critical citation level (CCL), we suggest using two real valued indicators to describe the shape of each area?s distribution: a high- and a low-impact measure defined over the set of articles with citations below or above the CCL. It is found that, when the CCL is fixed at the 80 percentile of the world citation distribution, the U.S. performs dramatically better than the EU and the RW according to both indicators in all scientific fields. This superiority generally increases as we move from the incidence to the intensity and the citation inequality aspects of the phenomena in question. Surprisingly, changes observed when the CCL is increased from the 80th to the 95th percentile are of a relatively small order of magnitude. Finally, it is found that international co-authorship increases the high-impact and reduces the low-impact level in the three geographical areas. This is especially the case for the EU and the RW when they cooperate with the U.S.
主题	Industrial Organization
关键词	Regional scientific/research performance Citation distributions Poverty and affluence Co-authorship
URL	https://cepr.org/publications/dp7886
来源智库	Centre for Economic Policy Research (United Kingdom)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/536723
推荐引用方式 GB/T 7714	Javier Ruiz-Castillo,Ignacio Ortuño-Ortin,Pedro Albarran. DP7886 High- and Low-Impact Citation Measures: Empirical Applications. 2010.