Navigating interdisciplinary research: Historical progression and contemporary challenges

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

Journal of Data and Information Science Pub Date : 2024-08-01 DOI:10.2478/jdis-2024-0025

Xiaoqiang Li, Fen Cai, Jintao Bao, Yuqing Jian, Zehui Sun, Xin Xie

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引用次数: 0

Abstract

Interdisciplinary research plays a crucial role in addressing complex problems by integrating knowledge from multiple disciplines. This integration fosters innovative solutions and enhances understanding across various fields. This study explores the historical and sociological development of interdisciplinary research and maps its evolution through three distinct phases: pre-disciplinary, disciplinary, and post-disciplinary. It identifies key internal dynamics, such as disciplinary diversification, reorganization, and innovation, as primary drivers of this evolution. Additionally, this study highlights how external factors, particularly the urgency of World War II and the subsequent political and economic changes, have accelerated its advancement. The rise of interdisciplinary research has significantly reshaped traditional educational paradigms, promoting its integration across different educational levels. However, the inherent contradictions within interdisciplinary research present cognitive, emotional, and institutional challenges for researchers. Meanwhile, finding a balance between the breadth and depth of knowledge remains a critical challenge in interdisciplinary education.

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驾驭跨学科研究：历史进程与当代挑战

跨学科研究通过整合多个学科的知识，在解决复杂问题方面发挥着至关重要的作用。这种整合促进了创新性的解决方案，并增强了对不同领域的理解。本研究探讨了跨学科研究的历史和社会学发展，并描绘了其在三个不同阶段的演变过程：前学科、学科和后学科。研究指出，学科多样化、重组和创新等关键的内部动力是这一演变的主要驱动力。此外，本研究还强调了外部因素，尤其是第二次世界大战的紧迫性以及随后的政治和经济变革如何加速了跨学科研究的发展。跨学科研究的兴起极大地重塑了传统的教育范式，促进了跨学科研究在不同教育层面的融合。然而，跨学科研究的内在矛盾给研究人员带来了认知、情感和制度上的挑战。同时，如何在知识的广度和深度之间找到平衡点，仍然是跨学科教育面临的重要挑战。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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