Hidden connections: Network effects on editorial decisions in four computer science journals

Giangiacomo Bravo, Mike Farjam, Francisco Grimaldo Moreno, Aliaksandr Birukou and Flaminio Squazzoni

Journal of Informetrics, 2018, vol. 12, issue 1, 101-112

Abstract: This paper aims to examine the influence of authors’ reputation on editorial bias in scholarly journals. By looking at eight years of editorial decisions in four computer science journals, including 7179 observations on 2913 submissions, we reconstructed author/referee-submission networks. For each submission, we looked at reviewer scores and estimated the reputation of submission authors by means of their network degree. By training a Bayesian network, we estimated the potential effect of scientist reputation on editorial decisions. Results showed that more reputed authors were less likely to be rejected by editors when they submitted papers receiving negative reviews. Although these four journals were comparable for scope and areas, we found certain journal specificities in their editorial process. Our findings suggest ways to examine the editorial process in relatively similar journals without recurring to in-depth individual data, which are rarely available from scholarly journals.

Keywords: Editorial bias; Network effects; Author reputation; Peer review; Bayesian network (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:12:y:2018:i:1:p:101-112

DOI: 10.1016/j.joi.2017.12.002

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