中国科技期刊研究 ›› 2024, Vol. 35 ›› Issue (9): 1317-1325. doi: 10.11946/cjstp.202404070331

• 评价与分析 • 上一篇    下一篇

编辑初审阶段中文科技论文的创新性评价研究——以水产学科技期刊为例

江睿()(), 艾红*()(), 章丽萍, 闫帅, 荣辉   

  1. 中国水产科学研究院南海水产研究所,广东省广州市海珠区新港西路231号 510300
  • 收稿日期:2024-04-07 修回日期:2024-07-10 出版日期:2024-09-15 发布日期:2024-10-28
  • 通讯作者: *艾红(ORCID:0000-0002-8825-7913),硕士,研究员,编辑部主任,E-mail:aihong@scsfri.ac.cn。
  • 作者简介:
    江睿(ORCID:0000-0002-8935-2760),博士,助理研究员,编辑,E-mail:
    章丽萍,硕士,助理研究员,编辑;
    闫帅,博士,助理研究员,编辑;
    荣辉,博士,助理研究员,编辑。
    作者贡献声明: 江 睿:提出选题,搜集和处理数据,搭建模型,撰写论文; 艾 红:修改、审定论文; 章丽萍:参与论文撰写; 闫 帅:参与数据处理; 荣 辉:参与文献收集。
  • 基金资助:
    中国农业期刊网研究基金项目“初审时中文科技论文创新性评价体系的构建与研究”(CAJW2022-015); 广东省科技计划项目“《南方水产科学》高质量科技期刊建设”(2024B1212100001)

Innovation evaluation of Chinese academic papers at editors′ initial review stage——Taking fishery scientific journals as examples

JIANG Rui()(), AI Hong*()(), ZHANG Liping, YAN Shuai, RONG Hui   

  1. South China Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, 231 Xingang West Road, Haizhu District, Guangzhou 510300, China
  • Received:2024-04-07 Revised:2024-07-10 Online:2024-09-15 Published:2024-10-28

摘要:

【目的】从创新扩散理论和创新性评价指标体系两个角度,构建编辑初审时中文科技论文的创新性定性和定量评价方法,助力科技论文评价体系的完善和中文科技期刊的质量提升。【方法】在创新扩散理论基础上,通过分析论文核心主题年度发文量曲线,对其创新性水平进行分类,对创新程度进行定性初判。根据4类13个论文创新性评价指标,依托《南方水产科学》2017—2020年刊出论文的748条同行评议结果、中国知网数据库和百度学术数据库,通过相关性分析,筛选出6个既可保障论文质量,又可反映创新性的关键评价指标。进一步运用熵值法计算指标权重,构建初审时论文创新性评价模型,对论文创新性进行定量评价。【结果】经验证,论文创新性评价模型可在编辑初审时,较为准确、高效地对单篇论文的创新性水平进行评价,且具有一定的普适性;而基于创新扩散曲线的创新性判断,则可为初审论文创新性评价提供快速的定性分析。【结论】所构建的中文科技论文初审创新性评价方法合理且易于应用,能够对科技论文创新性评价领域进行补充。

关键词: 创新性评价模型, 中文科技论文, 编辑初审, 指标体系, 创新扩散理论

Abstract:

[Purposes] This study aims to construct the qualitative and quantitative evaluation methods for the innovation of Chinese academic papers during the editors' initial review stage, using innovation diffusion theory and an academic innovation index system. This effort is intended to enhance the evaluation system for academic papers and improve the quality of Chinese scientific journals. [Methods] Building on the innovation diffusion theory, we classified the degree of innovation by analyzing the annual publication curve of core topics, enabling a preliminary qualitative judgment of the paper's innovativeness. We identified six characteristic indicators that ensure both paper quality and reflect innovation through correlation analysis, utilizing 13 academic innovation indices across four categories and leveraging 748 peer review outcomes from papers published in Southern China Fisheries Science from 2017 to 2020, alongside data from the CNKI and Baidu Academic databases. The entropy method was then applied to calculate index weights, establishing an academic innovation evaluation model for the initial review stage to quantitatively assess paper innovation. [Findings] The validation shows that this innovation evaluation model can accurately and efficiently assess the level of innovation of a paper at the editor's initial review stage with considerable universality. Additionally, the innovation assessment based on the innovation diffusion curve offers a rapid qualitative analysis for evaluating paper innovation at this stage. [Conclusions] The developed method for evaluating innovation in papers at the initial review stage is reasonable and practical, providing a valuable addition to the field of academic paper innovation evaluation.

Key words: Innovation evaluation model, Chinese academic paper, Editors' initial review stage, Index system, Innovation diffusion theory