首页> 外文期刊>Journal of informetrics >Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references
【24h】

Bibliometric approximation of a scientific specialty by combining key sources, title words, authors and references

机译:通过结合关键来源,标题词,作者和参考文献来对科学专业进行文献计量近似

获取原文
获取原文并翻译 | 示例
           

摘要

Bibliometric methods for the analysis of highly specialized subjects are increasingly investigated and debated. Information and assessments well-focused at the specialty level can help make important decisions in research and innovation policy. This paper presents a novel method to approximate the specialty to which a given publication record belongs. The method partially combines sets of key values for four publication data fields: source, title, authors and references. The approach is founded in concepts defining research disciplines and scholarly communication, and in empirically observed regularities in publication data. The resulting specialty approximation consists of publications associated to the investigated publication record via key values for at least three of the four data fields. This paper describes the method and illustrates it with an application to publication records of individual scientists. The illustration also successfully tests the focus of the specialty approximation in terms of its ability to connect and help identify peers. Potential tracks for further investigation include analyses involving other kinds of specialized publication records, studies for a broader range of specialties, and exploration of the potential for diverse applications in research and research policy context. (C) 2017 Elsevier Ltd. All rights reserved.
机译:用于高度专业化主题分析的文献计量方法日益受到研究和争论。专门针对专业水平的信息和评估可以帮助做出重要的研究和创新政策决策。本文提出了一种新颖的方法来近似给定出版物记录所属的专业。该方法部分组合了四个发布数据字段的键值集:来源,标题,作者和参考。该方法基于定义研究学科和学术交流的概念,以及根据经验观察到的出版物数据规律性。所得的专业近似值包括通过四个数据字段中至少三个的键值与调查的出版物记录相关联的出版物。本文介绍了该方法,并通过在单个科学家的出版记录中的应用举例说明了该方法。该图还通过连接和帮助识别对等点的能力成功地测试了特殊近似的焦点。可能进行进一步研究的轨迹包括涉及其他种类的专业出版物记录的分析,对更广泛专业的研究以及对研究和研究政策环境中各种应用的潜力的探索。 (C)2017 Elsevier Ltd.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号