图书情报工作 ›› 2018, Vol. 62 ›› Issue (24): 79-86.DOI: 10.13266/j.issn.0252-3116.2018.24.011

• 理论研究 • 上一篇    下一篇

知识生产视角下学术论文质量自动评审指标体系构建研究

祝琳琳1, 杜杏叶1,2,3, 李贺1   

  1. 1. 吉林大学管理学院 长春 130022;
    2. 中国科学院文献情报中心 北京 100190;
    3. 中国科学院大学图书情报与档案管理学 北京 100190
  • 收稿日期:2018-06-07 修回日期:2018-09-20 出版日期:2018-12-20 发布日期:2018-12-20
  • 通讯作者: 杜杏叶(ORCID:0000-0001-5016-0561),副研究馆员,副编审,博士研究生,通讯作者,E-mail:duxy@mail.las.ac.cn
  • 作者简介:祝琳琳(ORCID:0000-0003-3749-6954),博士研究生;李贺(ORCID:0000-0001-8847-3619),教授,博士生导师。
  • 基金资助:
    本文系中国科学院文献情报能力建设项目"新型出版研究"子项目"稿件自动评审研究"(项目编号:院1732)研究成果之一。

Study on the Construction of Index System for Automatic Review of Academic Paper Quality Under the Perspective of Knowledge Production

Zhu Linlin1, Du Xingye1,2,3, Li He1   

  1. 1. School of Management, Jilin University, Changchun 130022;
    2. National Science Library, Chinese Academy of Sciences, Beijing 100190;
    3. Department of Library, Information and Archives Management, University of Chinese Academy of Sciences, Beijing 100190
  • Received:2018-06-07 Revised:2018-09-20 Online:2018-12-20 Published:2018-12-20

摘要: [目的/意义]针对当前未发表学术论文质量的自动评审尚未形成统一的指标体系的问题,探索并建立一套具有引导性、科学性、客观性的论文质量自动评审指标体系,以提高评审效率。[方法/过程]在知识生产视角下,结合科学知识生产要素,分别从论文作者、参考文献、资金项目支持、选题、创新性、科学性、表达形式7个方面,构建论文质量自动评审指标体系,对其量化方法和技术进行简要说明,并运用主成分分析方法确定各项自动评审指标项权重及排序。[结果/结论]数据结果表明,论文科学性、创新性权重值均较高,论文表达形式中的摘要可读性、长度和参考文献所在期刊质量、新度同样是重要的自动评审因素,该结果能够为后续自动评审指标的量化处理提供借鉴。

关键词: 知识生产, 学术论文, 质量, 自动评审, 主成分分析

Abstract: [Purpose/significance] The automatic review of the quality of unpublished academic papers has not yet formed a unified index system, so this article explores and establishes a set of leading, scientific and objective index system for automatic review to improve the efficiency.[Method/process] From the perspective of knowledge production, combined with the factors of scientific knowledge production, the index system of academic paper quality for automatic review whose quantitative methods and techniques are briefly introduced, is constructed from seven aspects of author, reference, fund project support, selection of topics, innovation, scientificity and expression form. The principal component analysis method is used to determine the weight and the ranking of various automatic review indexes.[Result/conclusion] The results show that the weight value of the scientificity and innovation of a paper are high. The readability, length of the abstract and the quality and newness of the journal in which the references are published are also important factors. The results can provide references for the quantitative treatment of the follow-up automatic review indexes.

Key words: knowledge production, academic papers, quality, automatic review, principal component analysis

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