{"title":"Characterizing structure of cross-disciplinary impact of global disciplines: A perspective of the Hierarchy of Science","authors":"Ruolan Liu, Jin Mao, Gang Li, Yujie Cao","doi":"10.2478/jdis-2024-0008","DOIUrl":null,"url":null,"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.","PeriodicalId":44622,"journal":{"name":"Journal of Data and Information Science","volume":"46 1","pages":""},"PeriodicalIF":1.5000,"publicationDate":"2024-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Data and Information Science","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.2478/jdis-2024-0008","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
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.
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