Using network centrality measures to improve national journal classification lists

TL;DR

This paper explores how network centrality measures can improve national journal classification lists by identifying mismatches and biases, especially towards older journals, to better reflect journal importance and relevance.

Contribution

It introduces the use of network centrality measures to detect and correct classification mismatches in national journal lists, enhancing their accuracy.

Findings

Centrality measures can identify journal importance within fields.

Mismatches often involve older journals being overrepresented.

Measures help detect shifts from peripheral to core journals.

Abstract

In countries like Denmark and Spain classified journal lists are now being produced and used in the calculation of nationwide performance indicators. As a result, Danish and Spanish scholars are advised to contribute to journals of high 'authority' (as in the former) or those within a high class (as in the latter). This can create a few problems. The aim of this paper is to analyse the potential use of network centrality measures to identify possible mismatches of journal categories. It analysis the Danish National Authority List and the Spanish CIRC Classification. Based on a sample of Library and Information Science publications, it analyses centrality measures that can assess on the importance of journals to given fields, correcting mismatches in these classifications. We conclude by emphasising the use of these measures to better calibrate journal classifications as we observe a general bias in these lists towards older journals. Centrality measures can allow to identify periphery-to-core journals' transitions.