Characterizing structure of cross-disciplinary impact of global disciplines: A perspective of the Hierarchy of Science

IF 1.5 3区管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Journal of Data and Information Science Pub Date : 2024-02-05 DOI:10.2478/jdis-2024-0008

Ruolan Liu, Jin Mao, Gang Li, Yujie Cao

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引用次数: 0

Abstract

Purpose Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation. However, there is still a paucity of analysis about the essential characteristics of disciplines’ cross-disciplinary impact. Design/methodology/approach In this study, we define cross-disciplinary impact on one discipline as its impact to other disciplines, and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact. The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications, and the balance and disparity of cross-disciplinary impact were measured as well. To demonstrate the cross-disciplinary impact of the disciplines in science, we chose Microsoft Academic Graph (MAG) as the data source, and investigated the relationship between disciplines’ cross-disciplinary impact and their positions in the Hierarchy of Science (HOS). Findings Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure, and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines. Several bibliometric features that measure the hardness of a discipline, including the number of references, the number of cited disciplines, the citation distribution, and the Price index have a significant positive effect on the variety of cross-disciplinary impact. The number of references, the number of cited disciplines, and the citation distribution have significant positive and negative effects on balance and disparity, respectively. It is concluded that the less hard the discipline, the greater the cross-disciplinary impact, the higher balance and the lower disparity of cross-disciplinary impact. Research limitations In the empirical analysis of HOS, we only included five broad disciplines. This study also has some biases caused by the data source and applied regression models. Practical implications This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research, as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines. Originality/value This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.

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全球学科交叉影响结构的特点：科学层次的视角

目的跨学科领域已成为现代科学的推动力和科学创新的重要源泉。然而，关于学科交叉影响的基本特征的分析仍然很少。设计/方法/途径在本研究中，我们将某一学科的交叉影响定义为其对其他学科的影响，并参照多样性-平衡性-差异性三维框架来表征交叉学科影响的结构。学科交叉影响的多样性被定义为高交叉影响出版物的比例，同时还测量了交叉影响的平衡性和差异性。为了证明学科在科学领域的跨学科影响，我们选择了微软学术图谱（MAG）作为数据源，并研究了学科的跨学科影响与其在科学层次结构（HOS）中的位置之间的关系。研究结果分析结果表明，交叉学科影响力排名与 HOS 结构之间存在显著相关性，学科对其相邻学科产生的交叉学科影响力更大。衡量学科硬度的几个文献计量特征，包括参考文献数、被引学科数、引文分布和普赖斯指数，对跨学科影响的多样性有显著的正向影响。参考文献数、被引学科数和引文分布分别对平衡性和差异性有显著的正效应和负效应。结论是，学科难度越小，跨学科影响越大，跨学科影响的平衡性越高，差异性越小。研究局限性在居屋的实证分析中，我们只纳入了五大学科。由于数据来源和应用回归模型的原因，本研究也存在一些偏差。现实意义本研究有助于制定学科政策，促进跨学科研究的发展，并为预测学科的跨学科影响提供了新的见解。原创性/价值本研究为正确理解跨学科影响和学科融合的机制提供了一个新的视角。

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来源期刊

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