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      Popular and/or Prestigious? Measures of Scholarly Esteem

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          Abstract

          Citation analysis does not generally take the quality of citations into account: all citations are weighted equally irrespective of source. However, a scholar may be highly cited but not highly regarded: popularity and prestige are not identical measures of esteem. In this study we define popularity as the number of times an author is cited and prestige as the number of times an author is cited by highly cited papers. Information Retrieval (IR) is the test field. We compare the 40 leading researchers in terms of their popularity and prestige over time. Some authors are ranked high on prestige but not on popularity, while others are ranked high on popularity but not on prestige. We also relate measures of popularity and prestige to date of Ph.D. award, number of key publications, organizational affiliation, receipt of prizes/honors, and gender.

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          A Principal Component Analysis of 39 Scientific Impact Measures

          Background The impact of scientific publications has traditionally been expressed in terms of citation counts. However, scientific activity has moved online over the past decade. To better capture scientific impact in the digital era, a variety of new impact measures has been proposed on the basis of social network analysis and usage log data. Here we investigate how these new measures relate to each other, and how accurately and completely they express scientific impact. Methodology We performed a principal component analysis of the rankings produced by 39 existing and proposed measures of scholarly impact that were calculated on the basis of both citation and usage log data. Conclusions Our results indicate that the notion of scientific impact is a multi-dimensional construct that can not be adequately measured by any single indicator, although some measures are more suitable than others. The commonly used citation Impact Factor is not positioned at the core of this construct, but at its periphery, and should thus be used with caution.
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            Applying centrality measures to impact analysis: A coauthorship network analysis

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              Generalized Hirsch h-index for disclosing latent facts in citation networks

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                Author and article information

                Journal
                21 December 2010
                Article
                1012.4871
                fa2ce3ae-7d63-4287-ae43-fb5669b21376

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                26 pages, 5 figures
                cs.DL

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