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超越引用的文献计量学:介绍提及提取与分析

Bibliometrics beyond citations: introducing mention extraction and analysis.

作者信息

Petrovich Eugenio, Verhaegh Sander, Bös Gregor, Cristalli Claudia, Dewulf Fons, van Gemert Ties, IJdens Nina

机构信息

Department of Philosophy and Education Sciences, University of Turin, Turin, Italy.

Department of Philosophy, University of Tilburg, Tilburg, The Netherlands.

出版信息

Scientometrics. 2024;129(9):5731-5768. doi: 10.1007/s11192-024-05116-x. Epub 2024 Aug 2.

Abstract

Standard citation-based bibliometric tools have severe limitations when they are applied to periods in the history of science and the humanities before the advent of now-current citation practices. This paper presents an alternative method involving the extracting and analysis of to map and analyze links between scholars and texts in periods that fall outside the scope of citation-based studies. Focusing on one specific discipline in one particular period and language area-Anglophone philosophy between 1890 and 1979-we describe a procedure to create a by identifying, extracting, and disambiguating mentions in academic publications. Our mention index includes 1,095,765 mention links, extracted from 22,977 articles published in 12 journals. We successfully link 93% of these mentions to specific philosophers, with an estimated precision of 82% to 91%. Moreover, we integrate the mention index into a database named EDHIPHY, which includes data and metadata from multiple sources and enables multidimensional mention analyses. In the final part of the paper, we present four case studies conducted by domain experts, demonstrating the use and the potential of both EDHIPHY and mention analyses more generally.

摘要

当标准的基于引用的文献计量工具应用于当前引用实践出现之前的科学和人文学科历史时期时,存在严重局限性。本文提出了一种替代方法,涉及提取和分析[此处原文缺失具体内容],以绘制和分析基于引用的研究范围之外的时期中学者与文本之间的联系。以一个特定时期和语言区域(1890年至1979年间的英语国家哲学)中的一个特定学科为重点,我们描述了一种通过识别、提取和消除学术出版物中的提及歧义来创建[此处原文缺失具体内容]的程序。我们的提及索引包括从12种期刊上发表的22977篇文章中提取的1095765个提及链接。我们成功地将其中93%的提及与特定哲学家联系起来,估计精确度在82%到91%之间。此外,我们将提及索引集成到一个名为EDHIPHY的数据库中,该数据库包含来自多个来源的数据和元数据,并能够进行多维度提及分析。在本文的最后部分,我们展示了领域专家进行的四个案例研究,更全面地展示了EDHIPHY和提及分析的用途及潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3f24/11491420/fee0996225ae/11192_2024_5116_Fig1_HTML.jpg

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