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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.




Bravo, G., Farjam, M., Grimaldo Moreno, F., Birukou, A., & Squazzoni, F. (2018). Hidden connections: Network effects on editorial decisions in four computer science journals. Journal of Informetrics, 12(1), 101–112. https://doi.org/10.1016/j.joi.2017.12.002

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