Eigenfactor
The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. In a manner reminiscent of Google's Pagerank algorithm, journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals.[1] As a measure of importance, the Eigenfactor score scales with the total impact of a journal. All else equal, journals generating higher impact to the field have larger Eigenfactor scores.
Eigenfactor scores and Article Influence scores are calculated by eigenfactor.org, where they can be freely viewed. Eigenfactor scores are intended to give a measure of how likely a journal is to be used, and are thought to reflect how frequently an average researcher would access content from that journal.[1]
The Eigenfactor approach is thought to be more robust than the impact factor metric,[2] which purely counts incoming citations without considering the significance of those citations.[3] While the Eigenfactor scores is correlated with total citation count for medical journals,[4] these metrics provide significantly different information. For a given number of citations, citations from more significant journals will result in a higher Eigenfactor score.[5]
Eigenfactor scores are measures of a journal's importance. It can be used in combination with H-index to evaluate the work of individual scientists. The H-index is sometimes considered the most robust indicator of a scientist's productivity,[3] but a number of shortcomings of the index have been much-debated and corrected indices proposed.[6]
[edit] References
- ^ a b Bergstrom, C. T. (2007). "Eigenfactor: Measuring the value and prestige of scholarly journals". College & Research Libraries News 68 (5). http://crln.acrl.org/content/68/5/314.full.pdf+html.
- ^ Johan Bollen; Herbert Van de Sompel; Aric Hagberg; Ryan Chute (2009). "A principal component analysis of 39 scientific impact measures". arXiv:0902.2183v1 [cs.CY].
- ^ a b Fersht, A. (Apr 2009). "The most influential journals: Impact Factor and Eigenfactor". Proceedings of the National Academy of Sciences of the United States of America 106 (17): 6883–6884. Bibcode 2009PNAS..106.6883F. doi:10.1073/pnas.0903307106. ISSN 0027-8424. PMC 2678438. PMID 19380731. http://www.pubmedcentral.nih.gov/articlerender.fcgi?tool=pmcentrez&artid=2678438.
- ^ Davis, P. M. (2008). "Eigenfactor: Does the principle of repeated improvement result in better estimates than raw citation counts?". Journal of the American Society for Information Science and Technology (arxiv.org) 59 (13): 2186–2188. arXiv:0807.2678. doi:10.1002/asi.20943.
- ^ Jevin D. West; Theodore Bergstrom; Carl T. Bergstrom (2010). "Big Macs and Eigenfactor Scores: Don't Let Correlation Coefficients Fool You". arXiv:0911.1807v2 [cs.CY].
- ^ Harzing AW, 2008: Reflections on the h-index
[edit] External links
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