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

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.     

Did they sell their soul to the devil? Some comparative case-studies on academic entrepreneurs in the life sciences in Europe

This paper looks at eight comparative case-studies on academic entrepreneurs in life sciences conducted in Europe in 2008. The interviewees were selected from the KEINS database that lists all academic inventors from Italy, France, Sweden and the Netherlands who have one or more patent applications registered at the European Patent Office, 1978–2004. The case-studies show that institutions largely influence the way in which scientists are coping with the normative uncertainty typical of science and technology systemic boundaries in the life sciences today. By exploring new local solutions, the entrepreneurial achievements of scientists are also establishing new institutional practices. Our empirical analysis helps to reconsider traditional dichotomies in the sociological, economic and institutional literature, such as publications versus patents and academia versus industry, and to illustrate the creative role of academic entrepreneurs.     

The F3-index. Valuing reviewers for scholarly journals

This paper presents an index that measures reviewer contribution to editorial processes of scholarly journals. Following a metaphor of ranking algorithms in sports tournaments, we created an index that considers reviewers on different context-specific dimensions, i.e., report delivery time, the length of the report and the alignment of recommendations to editorial decisions. To test the index, we used a dataset of peer review in a multi-disciplinary journal, including 544 reviewers on 606 submissions in six years. Although limited by sample size, the test showed that the index identifies outstanding contributors and weak performing reviewers efficiently. Our index is flexible, contemplates extensions and could be incorporated into available scholarly journal management tools. It can assist editors in rewarding high performing reviewers and managing editorial turnover.     

Saint Matthew strikes again: An agent-based model of peer review and the scientific community structure

This paper investigates the impact of referee reliability on the quality and efficiency of peer review. We modeled peer review as a process based on knowledge asymmetries and subject to evaluation bias. We tested various levels of referee reliability and different mechanisms of reviewing effort distribution among agents. We also tested different scientific community structures (cohesive vs. parochial) and competitive science environments (high vs. low competition). We found that referee behavior drastically affects peer review and an equal distribution of the reviewing effort is beneficial only if the scientific community is homogeneous and referee reliability is the rule. We also found that the Matthew effect in the allocation of resources and credit is inherent to a ‘winner takes all’ well functioning science system, more than a consequence of evaluation bias.