Modelling in engineering: A citation context analysis

IF 1.8 4区 管理学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS Journal of Information Science Pub Date : 2023-07-10 DOI:10.1177/01655515231184833
G. Schweiger, Lynn Thiermeyer
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引用次数: 0

Abstract

Purely quantitative citation measures are widely used to evaluate research grants, to compare the output of researcher or to benchmark universities. The intuition that not all citations are the same, however, can be illustrated by two examples. First, studies have shown that erroneous or controversial papers have higher citation counts. Second, does a high-level citation in an introduction have the same impact as a reference to a paper that serves as a conceptual starting point? Companions to purely quantitative measures are the so-called citation context analyses which aim to obtain a better understanding of the link between citing and cited work. In this article, we propose a classification scheme for citation context analysis in the field of modelling in engineering. The categories were defined based on an extensive literature review and input from experts in the field of modelling. We propose a detailed scheme with six categories ( Perfunctory, Background Information, Comparing/Confirming, Critique/Refutation, Inspiring, Using/Expanding) and a simplified scheme with three categories ( High-level, Critical Analysis, Extending) that can be used within automatic classification approaches. The results of manually classifying 129 randomly selected citations show that 87% of citations fall into the high-level category. This study confirms that critical citations are not common in written academic discourse, even though criticism is essential for scientific progress and knowledge construction.
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工程建模:引文语境分析
纯粹的定量引用措施被广泛用于评估研究资助,比较研究人员或基准大学的产出。然而,并非所有引用都是相同的直觉可以用两个例子来说明。首先,研究表明,错误或有争议的论文有更高的引用率。第二,引言中的高水平引文是否与作为概念起点的论文参考文献具有相同的影响?所谓的引文上下文分析是纯定量测量的伙伴,旨在更好地理解引文与被引作品之间的联系。在本文中,我们提出了一个用于工程建模领域引文上下文分析的分类方案。这些类别是基于广泛的文献回顾和建模领域专家的输入来定义的。我们提出了一个详细的方案,包括六个类别(肤浅,背景信息,比较/确认,批评/反驳,鼓舞,使用/扩展)和一个简化的方案,包括三个类别(高级,关键分析,扩展),可以在自动分类方法中使用。对随机选取的129篇引文进行人工分类,结果显示87%的引文属于高水平类别。本研究证实,尽管批评对科学进步和知识建构至关重要,但在书面学术话语中,批评引用并不常见。
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来源期刊
Journal of Information Science
Journal of Information Science 工程技术-计算机:信息系统
CiteScore
6.80
自引率
8.30%
发文量
121
审稿时长
4 months
期刊介绍: The Journal of Information Science is a peer-reviewed international journal of high repute covering topics of interest to all those researching and working in the sciences of information and knowledge management. The Editors welcome material on any aspect of information science theory, policy, application or practice that will advance thinking in the field.
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