Eigenfactor : Does the Principle of Repeated Improvement Result in Better Journal Impact Estimates than Raw Citation Counts?

TL;DR

This paper investigates whether the Eigenfactor method, which uses an iterative algorithm similar to PageRank, produces different journal impact rankings than simple citation counts, questioning the added value of repeated improvement.

Contribution

It compares Eigenfactor's iterative impact estimation with raw citation counts to assess if the former offers significantly different journal rankings.

Findings

Eigenfactor provides different rankings than raw citation counts.

Repeated improvement influences impact estimates.

Eigenfactor may better reflect journal influence.

Abstract

Eigenfactor.org, a journal evaluation tool which 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 ISI).