期刊文献+

基于差异性视角的期刊区分性测度及分析研究 被引量:6

Measurement and Analysis of Journal Discriminative Capacity Based on Difference
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摘要 当前的期刊评价研究主要集中在影响力、声誉、质量等方面,针对期刊内容差异性的定量研究相对较少。本文从差异性视角来对期刊进行评价分析,提出期刊区分度指标来对期刊内容的差异程度进行度量。以LIS (library and information science)等5个学科的各20种核心期刊为研究对象,首先对LIS期刊的内容差异性进行定量分析与评价,然后从时间维度对LIS学科期刊区分度的变化规律进行了探测,最后从学科维度对不同学科期刊个体和总体区分度的特征进行了分析和探讨。实验结果表明,利用该指标能够很好地度量期刊研究内容差异性,期刊区分度在时间维度上呈现出明显的变化规律,不同学科期刊个体和总体的区分度均具有显著的学科特征。 Current research on journal evaluation focuses primarily on measurement of influence, reputation, quality, and similar concepts. This paper evaluates journals from a new perspective based on content difference, proposing journal discriminative capacity as an index to measure differences in academic journals content. Twenty core journals for each of the five disciplines, which are library and information science(LIS), aerospace, biology, art and law, were selected as research objects. First, content differences between LIS journals were quantitatively analyzed and evaluated. Trends in LIS journalsdiscriminative capacity over time were then explored. Finally, the characteristics of individual and overall discriminative capacity of journals from different disciplines were analyzed and discussed. The results show that the index is highly effective in measuring differences in journal research content, that journals discriminative capacity shows obvious trends over time, and that individual and overall journal discriminative capacity has significant disciplinary characteristics.
作者 张宝隆 王昊 邓三鸿 苏新宁 Zhang Baolong;Wang Hao;Deng Sanhong;Su Xinning(School of Information Management,Nanjing University,Nanjing 210023;Jiangsu Key Laboratory of Data Engineering&Knowledge Service,Nanjing 210023)
出处 《情报学报》 CSSCI CSCD 北大核心 2020年第3期284-296,共13页 Journal of the China Society for Scientific and Technical Information
基金 国家社会科学基金重大招标项目“情报学学科建设与情报工作未来发展路径研究”(17ZDA291) 国家自然科学基金青年科学基金项目“面向学术资源的TSD与TDC测度及分析研究”(71503121)。
关键词 期刊区分度 差异性 期刊评价 层次聚类分析 PCA降维 journal discriminative capacity journal difference journal assessment hierarchical clustering analysis PCA dimension reduction
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