信息资源管理学报 ›› 2023, Vol. 13 ›› Issue (2): 125-134.doi: 10.13365/j.jirm.2023.02.125

• 研究论文 • 上一篇    下一篇

同行评议质量新探:同行评议意见挖掘研究综述

王勇臻 王贤文   

  1. 大连理工大学科学学与科技管理研究所暨WISE实验室,大连,116024
  • 出版日期:2023-03-26 发布日期:2023-04-20
  • 作者简介:王勇臻,副教授,硕士生导师,研究方向为文本挖掘与信息计量;王贤文(通讯作者),教授,博士生导师,研究方向为科学计量与科技管理,Email: xianwenwang@dlut.edu.cn。
  • 基金资助:
    本文受辽宁省社会科学规划基金项目(L21CTQ001)资助。

A New Probe into Peer Review Quality: A Narrative Review of Research on Peer Review Comment Mining

Wang Yongzhen Wang Xianwen   

  1. WISE Lab, Institute of Science of Science and S&T Management, Dalian University of Technology, Dalian 116024
  • Online:2023-03-26 Published:2023-04-20

摘要: 在开放式同行评议模式蔚然成风与自然语言处理技术日新月异的双重驱动下,同行评议质量的研究范式正在酝酿改变——同行评议意见挖掘已悄然兴起。纵观此变迁轨迹,在文献回顾的基础之上,本文系统地梳理了同行评议意见挖掘的代表性研究成果,旨在阐明这一新兴研究方向的概貌特征以及随之而来的机遇和挑战,为后续的探索实践提供借鉴与参考。结果显示,目前有关同行评议意见挖掘的研究工作主要围绕同行评议意见的预测能力、情感表达以及论辩逻辑三个方面展开,着重探讨各种文本挖掘方法在被用于解析同行评议意见时的可行性与有效性,且所使用的数据样本限于计算机科学领域的国际会议论文(英文)。

关键词: 同行评议, 同行评议质量, 同行评议意见挖掘, 文本挖掘, 科技评价

Abstract: A paradigm shift in peer review quality research——driven by the progressive popularity of open peer review and the rapid advancement of natural language processing jointly——is happening, while peer review comment mining (PRCM) is becoming universal unobtrusively. This paper comprehensively summarizes the relevant literature on PRCM to clarify the general characteristics of this emerging research direction, plus the opportunities and challenges it may bring, with the purpose of providing a reference guide for subsequent studies. The literature review shows, nowadays, the vast majority of PRCM-related research focuses on the investigation of prediction capability, emotion expression, and argumentation logic with respect to peer review comments, with special attention paid to the feasibility and validity of various text mining methods when applied to interpreting and analyzing peer review comments. In addition, the data samples used in PRCM-related research are limited to conference papers on computer science (in English).

Key words: Peer review, Peer review quality, Peer review comment mining, Text mining, Science and technology evaluation

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