The need to develop tailored tools for improving the quality of thematic bibliometric analyses: Evidence from papers published in Sustainability and Scientometrics

IF 1.5 3区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE Journal of Data and Information Science Pub Date : 2023-09-22 DOI:10.2478/jdis-2023-0021
Alvaro Cabezas-Clavijo, Yusnelkis Milanés-Guisado, Ruben Alba-Ruiz, Ángel M. Delgado-Vázquez
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Abstract

Abstract Purpose The aim of this article is to explore up to seven parameters related to the methodological quality and reproducibility of thematic bibliometric research published in the two most productive journals in bibliometrics, Sustainability (a journal outside the discipline) and Scientometrics, the flagship journal in the field. Design/methodology/approach The study identifies the need for developing tailored tools for improving the quality of thematic bibliometric analyses, and presents a framework that can guide the development of such tools. A total of 508 papers are analysed, 77% of Sustainability, and 23% published in Scientometrics, for the 2019-2021 period. Findings An average of 2.6 shortcomings per paper was found for the whole sample, with an almost identical number of flaws in both journals. Sustainability has more flaws than Scientometrics in four of the seven parameters studied, while Scientometrics has more shortcomings in the remaining three variables. Research limitations The first limitation of this work is that it is a study of two scientific journals, so the results cannot be directly extrapolated to the set of thematic bibliometric analyses published in journals from all fields. Practical implications We propose the adoption of protocols, guidelines, and other similar tools, adapted to bibliometric practice, which could increase the thoroughness, transparency, and reproducibility of this type of research. Originality/value These results show considerable room for improvement in terms of the adequate use and breakdown of methodological procedures in thematic bibliometric research, both in journals in the Information Science area and journals outside the discipline.
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为提高专题文献计量分析的质量开发量身定制的工具的必要性:来自《可持续性》和《科学计量学》发表的论文的证据
摘要:本文旨在探讨在文献计量学领域最多产的两本期刊《Sustainability》(学科外期刊)和该领域的旗舰期刊《科学计量学》(Scientometrics)上发表的主题文献计量学研究的方法质量和可重复性相关的七个参数。设计/方法/方法本研究确定需要开发适合的工具来提高专题文献计量分析的质量,并提出了一个可以指导开发这种工具的框架。在2019-2021年期间,共分析了508篇论文,其中77%为Sustainability, 23%发表在Scientometrics上。在整个样本中,平均每篇论文有2.6个缺陷,两种期刊的缺陷数量几乎相同。在研究的七个参数中,可持续性在四个参数上比科学计量学存在更多缺陷,而科学计量学在其余三个变量上存在更多缺陷。这项工作的第一个限制是它是对两种科学期刊的研究,因此结果不能直接外推到在所有领域的期刊上发表的主题文献计量分析集。我们建议采用适合文献计量学实践的协议、指南和其他类似工具,这可以增加这类研究的彻彻性、透明度和可重复性。原创性/价值这些结果表明,在专题文献计量学研究的方法程序的充分使用和分解方面,无论是在资料科学领域的期刊还是在该学科以外的期刊上,都有很大的改进余地。
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来源期刊
Journal of Data and Information Science
Journal of Data and Information Science INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
3.50
自引率
6.70%
发文量
495
期刊介绍: JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data. The main areas of interest are: (1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis. (2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences. (3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management. Specific topic areas may include: Knowledge organization Knowledge discovery and data mining Knowledge integration and fusion Semantic Web metrics Scientometrics Analytic and diagnostic informetrics Competitive intelligence Predictive analysis Social network analysis and metrics Semantic and interactively analytic retrieval Evidence-based policy analysis Intelligent knowledge production Knowledge-driven workflow management and decision-making Knowledge-driven collaboration and its management Domain knowledge infrastructure with knowledge fusion and analytics Development of data and information services
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