Levene, Mark and Harris, Martyn and Fenner, Trevor (2020) A two-dimensional bibliometric index reflecting both quality and quantity. Scientometrics 123 , pp. 1235-1246. ISSN 0138-9130.
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Abstract
We propose a two-dimensional bibliometric index that strikes a balance between quan- tity (as measured by the number of publications of a researcher) and quality (as measured by the number of citations to those publications). While the square of h-index is deter- mined by the maximum area square that fits under the citation curve of an author when plotting the number of citations in decreasing order, the rec-index is determined by the maximum area rectangle that fits under the curve. In this context we may distinguish between authors with a few very highly-cited publications, who may have carried out some in uential research, and prolific authors, who may have many publications but fewer ci- tations per publication. The in uence of a researcher may be measured via a restricted version of the rec-index, the recI -index, which is the maximum area vertical rectangle that fits under the citation curve. Similarly, the prolificity of a researcher may be mea- sured via the recP -index, which is the maximum area horizontal rectangle that fits under the citation curve. This leads to the proposal of the two-dimensional bibliometric index (recI ; recP ), which captures both aspects of a researcher's output. We present a compre- hensive empirical analysis of this two-dimensional index on two datasets: a large set of Google Scholar profiles (representing \typical" researchers) and a small set of Nobel prize winners. Our results demonstrate the potential of this two-dimensional index, since for both data sets there is a statistically significant number of researchers for whom recI is greater than recP . In particular, for nearly 25% of the Google Scholar researchers and for nearly 60% of the Nobel prize winners, recI is greater than recP .
Metadata
| Item Type: | Article |
|---|---|
| Additional Information: | The final publication is available at Springer via the link above. |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Research Centres and Institutes: | Data Analytics, Birkbeck Institute for |
| Depositing User: | Administrator |
| Date Deposited: | 01 May 2020 12:42 |
| Last Modified: | 09 Aug 2023 12:48 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/31824 |
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