An Accurate and Impartial Expert Assignment Method for Scientific Project Review

Author:

Yue Mingliang1,Tian Kailin2,Ma Tingcan1

Affiliation:

1. Wuhan Documentation and Information Center, Chinese Academy of Sciences , Wuhan , 430071 , China

2. Library, South West Forestry University , Kunming , 650224 , China

Abstract

Abstract Purpose This paper proposes an expert assignment method for scientific project review that considers both accuracy and impartiality. As impartial and accurate peer review is extremely important to ensure the quality and feasibility of scientific projects, enhanced methods for managing the process are needed. Design/methodology/approach To ensure both accuracy and impartiality, we design four criteria, the reviewers’ fitness degree, research intensity, academic association, and potential conflict of interest, to express the characteristics of an appropriate peer review expert. We first formalize the expert assignment problem as an optimization problem based on the designed criteria, and then propose a randomized algorithm to solve the expert assignment problem of identifying reviewer adequacy. Findings Simulation results show that the proposed method is quite accurate and impartial during expert assignment. Research limitations Although the criteria used in this paper can properly show the characteristics of a good and appropriate peer review expert, more criteria/conditions can be included in the proposed scheme to further enhance accuracy and impartiality of the expert assignment. Practical implications The proposed method can help project funding agencies (e.g. the National Natural Science Foundation of China) find better experts for project peer review. Originality/value To the authors’ knowledge, this is the first publication that proposes an algorithm that applies an impartial approach to the project review expert assignment process. The simulation results show the effectiveness of the proposed method.

Publisher

Walter de Gruyter GmbH

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. The development of a reviewer selection method: a multi-level hesitant fuzzy VIKOR and TOPSIS approaches;Journal of Ambient Intelligence and Humanized Computing;2021-09-18

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