Examining academic ranking and inequality in library and information science through faculty hiring networks

Yongjun Zhu, Erjia Yan

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

In this study, we examine academic ranking and inequality in library and information science (LIS) using a faculty hiring network of 643 faculty members from 44 LIS schools in the United States. We employ four groups of measures to study academic ranking, including adjacency, placement and hiring, distance-based measures, and hubs and authorities. Among these measures, closeness and hub measures have the highest correlation with the U.S. News ranking (r = 0.78). We study academic inequality using four distinct methods that include downward/upward placement, Lorenz curve, cliques, and egocentric networks of LIS schools and find that academic inequality exists in the LIS community. We show that the percentage of downward placement (68%) is much higher than that of upward placement (22%); meanwhile, 20% of the 30 LIS schools that have doctoral programs produced nearly 60% of all LIS faculty, with a Gini coefficient of 0.53. We also find cliques of highly ranked schools and a core/periphery structure that distinguishes LIS schools of different ranks. Overall, LIS faculty hiring networks have considerable value in deriving credible academic ranking and revealing faculty exchange within the field.

Original languageEnglish
Pages (from-to)641-654
Number of pages14
JournalJournal of Informetrics
Volume11
Issue number2
DOIs
Publication statusPublished - 2017 May 1

Bibliographical note

Funding Information:
We thank Dr. Cassidy R. Sugimoto of Indiana University for her valuable feedback on an earlier version of this paper. This project was made possible in part by the Institute of Museum and Library Services (Grant Award Number: RE-07-15-0060-15), for the project titled ?Building an entity-based research framework to enhance digital services on knowledge discovery and delivery?.

Publisher Copyright:
© 2017 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Library and Information Sciences

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