摘要
【目的/意义】基于载文量和影响因子的期刊分区研究一直较少受到关注,研究各区域特征有助于了解每类期刊的特点,并为期刊的长期健康发展提出建议。【方法/过程】首先通过传统回归和分位数回归两种方法研究总体载文量和影响因子的关系,然后将期刊基于载文量与影响因子关系进行分类,最后运用Kruskal-Wallis检验和多重比较方法分析各个区域的特征。【结果/结论】期刊的载文量与影响因子从总体看呈倒U型关系;基于载文量和影响因子的期刊分类方法对每个区的期刊进行评价发现:Ⅱ区最佳,Ⅰ区次之,Ⅳ区较弱,Ⅲ区最弱;高载文量期刊影响因子整体偏低,大部分高影响因子期刊载文量较大;不同区域期刊在载文策略上有所不同且期刊质量差距较大。【创新/局限】这种期刊分区方法可以直观地表征期刊的载文量和影响因子在该领域中所处的位置,也可以直观地看出4个分区的优劣,推进了文献计量理论与方法研究,为拓宽期刊分类方法提供一个新的思路。
【Purpose/significance】Research on journal ranking based on the article quantity and impact factor has received little attention.The study of the characteristics of each region is helpful to understand the characteristics of each type of journals and put forward suggestions for the long-term healthy development of journals.【Method/process】Firstly,traditional regression and quantile regression were used to analyze the relationship between the article quantity and the impact factor of all journals,then the journals are classified based on the relationship between the article quantity and impact factor,and the characteristics of each region were analyzed by Kruskal-Wallis test and multiple comparison method.【Result/conclusion】There is an inverted U-shaped relationship between the article quantity and impact factor;the journal classification method based on article quantity and impact factor can evaluate the quality of journals in each region:regionⅡis the best,regionⅠis the second,regionⅣis the third,and regionⅢis the worst;the impact factors of high article quantity journals are generally low,and most high impact factor journals have large article quantities;the journals in different regions have different strategies in article quantity and the quality of journals in different regions varies greatly.【Innovation/limitation】The journal ranking method can visually characterize the position of article quantity and impact factor in this field,and can also intuitively see the advantages and disadvantages of the four partitions.The periodical classification method promotes the research of bibliometric theory and method,and provides a new idea for broadening the periodical classification method.
作者
俞立平
周朦朦
李隆铖
YU Liping;ZHOU Mengmeng;LI Longcheng(School of Statistics and Mathematics,Zhejiang Gongshang University,Hangzhou 310018,China;School of Computer Science and Communication,Jiangsu Univeristy,Zhenjiang 212023,China)
出处
《情报科学》
CSSCI
北大核心
2023年第4期141-148,163,共9页
Information Science
基金
国家社科基金后期资助“学术期刊评价——指标创新与方法研究”(21FTQB016)。