Indicators of the Interdisciplinarity of Journals: Diversity, Centrality, and Citations

TL;DR

This paper introduces and evaluates network and citation-based indicators, including betweenness centrality and Shannon entropy, to measure the interdisciplinarity of journals, aiming to fill a gap in existing journal metrics.

Contribution

The study proposes a new diversity measure combining citation distribution and network distances, and compares various indicators for effectiveness in assessing journal interdisciplinarity.

Findings

Betweenness centrality in symmetrical networks outperforms in asymmetrical ones.

Shannon entropy is a better vector-based indicator than Gini coefficient but is size-sensitive.

The new diversity measure yields reasonable results with cosine-based distances.

Abstract

A citation-based indicator for interdisciplinarity has been missing hitherto among the set of available journal indicators. In this study, we investigate network indicators (betweenness centrality), journal indicators (Shannon entropy, the Gini coefficient), and more recently proposed Rao-Stirling measures for "interdisciplinarity." The latter index combines the statistics of both citation distributions of journals (vector-based) and distances in citation networks among journals (matrix-based). The effects of various normalizations are specified and measured using the matrix of 8,207 journals contained in the Journal Citation Reports of the (Social) Science Citation Index 2008. Betweenness centrality in symmetrical (1-mode) cosine-normalized networks provides an indicator outperforming betweenness in the asymmetrical (2-mode) citation network. Among the vector-based indicators, Shannon entropy performs better than the Gini coefficient, but is sensitive to size. Science and Nature, for example, are indicated at the top of the list. The new diversity measure provides reasonable results when (1 - cosine) is assumed as a measure for the distance, but results using Euclidean distances were difficult to interpret.