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[目的 /意义]论文被引频次只能反映论文的宏观影响力,无法揭示论文在他人研究中的具体作用和影响,因此,本文提出从引用内容的主题和功能两方面对论文的影响力进行分析。[方法 /过程]以2014年诺贝尔生理学或医学奖获得者J.O’Keefe的高被引论文为实例,首先,采用文献计量学方法对引用内容主题进行分析;对其,影响范围及领域进行可视化分析;其次,从引用性质和功能角度,将引用内容分成正面引用、负面引用和中性引用;最后,将中性引用进一步划分为3类,分别是研究背景介绍、理论基础和实验基础。[结果 /结论]结果表明,共词分析可以很好地表达论文影响的主题领域;引用内容的分类可以提供一篇论文被引用的多方面原因。在本实验中没有负面引用,多于10%的引用为正面引用,大约50%的中性引用都是作者在研究背景章节中介绍与施引文献相关的研究工作。
[Purpose / Significance] The citation times of the essay can only reflect the macroscopic influence of the essay, and can not reveal the specific function and influence of the essay in the research of others. Therefore, this paper proposes to make the influence of the essay on the subject and function from the citation content analysis. [Method / Procedure] Taking the highly cited papers of J.O’Keefe, winner of Nobel Prize in Physiology or Medicine in 2014 as an example, firstly, using bibliometrics to analyze the subject matter of quotation; analyze its influential scope and field Secondly, from the perspective of reference nature and function, the citation is divided into frontal references, negative references and neutral references. Finally, the neutral references are further divided into three categories, which are the background of the research, the theoretical basis and the experimental basis . [Results / Conclusions] The results show that co-word analysis can well express the thematic areas affected by the essay. The classification of quoted content can provide a variety of reasons why the essay is cited. There are no negative citations in this experiment, more than 10% citations are positive citations, and about 50% of neutral citations are the author’s research related to citing articles in the research background section.