Archives des mots-clés : Olgica Nedic

Peer Review of Reviewers: The Author’s Perspective

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Authors : Ivana Drvenica, Giangiacomo Bravo, Lucija Vejmelka, Aleksandar Dekanski, Olgica Nedić

The aim of this study was to investigate the opinion of authors on the overall quality and effectiveness of reviewers’ contributions to reviewed papers. We employed an on-line survey of thirteen journals which publish articles in the field of life, social or technological sciences.

Responses received from 193 authors were analysed using a mixed-effects model in order to determine factors deemed the most important in the authors’ evaluation of the reviewers. Qualitative content analysis of the responses to open questions was performed as well.

The mixed-effects model revealed that the authors’ assessment of the competence of referees strongly depended on the final editorial decision and that the speed of the review process was influential as well.

In Ordinary Least Squares (OLS) analysis on seven questions detailing authors’ opinions, perception of review speed remained a significant predictor of the assessment. In addition, both the perceived competence and helpfulness of the reviewers significantly and positively affected the authors’ evaluation.

New models were used to re-check the value of these two factors and it was confirmed that the assessment of the competence of reviewers strongly depended on the final editorial decision.

URL : Peer Review of Reviewers: The Author’s Perspective

Alternative location : https://www.mdpi.com/2304-6775/7/1/1

Artificial intelligence in peer review: How can evolutionary computation support journal editors?

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Authors : Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak, Marcel Ausloos, Olgica Nedic

With the volume of manuscripts submitted for publication growing every year, the deficiencies of peer review (e.g. long review times) are becoming more apparent. Editorial strategies, sets of guidelines designed to speed up the process and reduce editors workloads, are treated as trade secrets by publishing houses and are not shared publicly.

To improve the effectiveness of their strategies, editors in small publishing groups are faced with undertaking an iterative trial-and-error approach. We show that Cartesian Genetic Programming, a nature-inspired evolutionary algorithm, can dramatically improve editorial strategies.

The artificially evolved strategy reduced the duration of the peer review process by 30%, without increasing the pool of reviewers (in comparison to a typical human-developed strategy).

Evolutionary computation has typically been used in technological processes or biological ecosystems. Our results demonstrate that genetic programs can improve real-world social systems that are usually much harder to understand and control than physical systems.

URL : https://arxiv.org/abs/1712.01682

Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals. II. An ARCH econometric-like modeling

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Authors : Marcel Ausloos, Olgica Nedic, Aleksandar Dekanski, Maciej J. Mrowinski, Piotr Fronczak, Agata Fronczak

This paper aims at providing a statistical model for the preferred behavior of authors submitting a paper to a scientific journal. The electronic submission of (about 600) papers to the Journal of the Serbian Chemical Society has been recorded for every day from Jan. 01, 2013 till Dec. 31, 2014, together with the acceptance or rejection paper fate.

Seasonal effects and editor roles (through desk rejection and subfield editors) are examined. An ARCH-like econometric model is derived stressing the main determinants of the favorite day-of-week process.

URL : https://arxiv.org/abs/1611.04639