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Considering author sequence in all-author co-citation analysis
Information Processing & Management ( IF 8.6 ) Pub Date : 2020-06-18 , DOI: 10.1016/j.ipm.2020.102300
Yi Bu , Binglu Wang , Zaida Chinchilla-Rodríguez , Cassidy R. Sugimoto , Yong Huang , Win-bin Huang

Author co-citation analysis (ACA) is a commonly used method to map knowledge domains and depict scientific intellectual structures. Although all authors’ information has been considered in previous studies, ACA does not distinguish credits of different collaborators within a team. Authors’ sequence in a publication illustrates their contributions and specialty of research, which offers more information as inputs of ACA. This paper considers author sequence in ACA and proposes a sequence-based ACA method. By assigning various weight values to authors with different sequences, this proposed method considers distinct contributions of co-authors influencing the effect of ACA. Extra weight is given to corresponding authors, beyond their sequence, to acknowledge their additional contributions. Results of the empirical study based on the data from the field of Library and Information Science show many details on the visualization maps of the proposed methods, such as the number of sub-fields, the position of sub-fields, the position of authors, and clarity and interpretability of visualization maps. Meanwhile, the current paper proposes a novel framework of evaluating knowledge domain maps with both quantitative and qualitative facets.



中文翻译:

在全作者共引分析中考虑作者顺序

作者同被引分析(ACA)是映射知识领域和描述科学知识结构的常用方法。尽管以前的研究已经考虑了所有作者的信息,但是ACA并未区分团队中不同合作者的信誉。作者在出版物中的顺序说明了他们的贡献和研究专长,它提供了更多的信息作为ACA的输入。本文考虑了ACA中的作者序列,并提出了一种基于序列的ACA方法。通过将不同的权重值分配给具有不同序列的作者,此提议的方法考虑了影响ACA效果的共同作者的不同贡献。给予作者额外的重视,超越他们的顺序,以表彰他们的额外贡献。根据图书馆和情报学领域的数据进行的实证研究结果显示,在所提出方法的可视化地图上有许多细节,例如子领域的数量,子领域的位置,作者的位置,可视化地图的清晰度和可解释性。同时,本文提出了一个新颖的框架来评估具有定量和定性两个方面的知识领域图。

更新日期:2020-06-18
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