Eigenfactor: Does the principle of repeated improvement result in better estimates than raw citation counts?

2008/07/31 by Philip M. Davis · 2 citations
Computer Science · Decision Sciences · #Web Data Mining and Analysis #Web visibility and informetrics #cs.DB #cs.DL #scientometrics and bibliometrics research

paper · pdf · doi:10.1002/asi.20943

published as Journal of the American Society for Information Science & Technology (2008) v59 n12 p.2186-2188 · bibliographic information corrected

openalex publication_date 2008/08/05 · arxiv created 2008/10/27 · arxiv updated 2009/12/01 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

Abstract Eigenfactor.org, a journal evaluation tool that uses an iterative algorithm to weight citations (similar to the PageRank algorithm used for Google), has been proposed as a more valid method for calculating the impact of journals. The purpose of this brief communication is to investigate whether the principle of repeated improvement provides different rankings of journals than does a simple unweighted citation count (the method used by the Institute for Scientific Information@ [ISI]).

Citations

• The Matthew Effect in Science, II: Cumulative Advantage and the Symbolism of Intellectual Property

Cited by

• Big Macs and Eigenfactor Scores: Don't Let Correlation Coefficients Fool You
• A review of theory and practice in scientometrics

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• Big Macs and Eigenfactor Scores: Don't Let Correlation Coefficients Fool You
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