信息资源管理学报 ›› 2024, Vol. 14 ›› Issue (4): 70-85.doi: 10.13365/j.jirm.2024.04.070

• 研究论文 • 上一篇    下一篇

科学突破主题的学科交叉演化特征分析

杨俊浩1　许海云1　王超1　刘春江2　张慧玲3　谭晓4   

  1. 1.山东理工大学管理学院，淄博，255000
    2.中国科学院成都文献情报中心，成都，610299
    3.太原市图书馆，太原，030024
    4.北京市科学技术研究院科技情报研究所，北京，100089
  • 出版日期:2024-07-26 发布日期:2024-08-14
  • 作者简介:杨俊浩，硕士生，研究方向为科技创新管理；许海云，博士，教授，研究方向为科技与产业情报分析；王超（通讯作者），博士，副教授，研究方向为科技与产业情报分析，Email: kingtaoist@yeah.net；刘春江，博士，高级工程师，研究方向为情报技术与分析；张慧玲，硕士，馆员，研究方向为科学计量；谭晓，博士，副研究员，研究方向为科学计量。
  • 基金资助:
    本文系国家自然科学基金项目“基于弱信号时效网络演化分析的变革性科技创新主题早期识别方法研究”（72274113），山东省社科规划研究项目“面向颠覆性技术早期识别的弱信号演化模式与机制研究”（23CTQJ07），科技大数据湖北省重点实验室（E3KF291001）研究成果之一。

Evolutionary Characteristics of the Interdisciplinary Research in Scientific Breakthrough Topics

Yang Junhao1　Xu Haiyun1　Wang Chao1　Liu Chunjiang2　Zhang Huiling3　Tan Xiao4   

  1. 1.Business School, Shandong University of Technology, Zibo, 255000
    2.Chengdu Documentation and Information Center, Chinese Academy of Science, Chengdu, 610299
    3.Taiyuan Library, Taiyuan, 030024
    4.Beijing Institute of Science and Technology Information, Beijing Academy of Science and Technology, Beijing, 100089
  • Online:2024-07-26 Published:2024-08-14
  • About author:Yang Junhao, M.S.candidate, research direction in science and technology innovation management; Xu Haiyun, Ph.D., professor, research direction in science and industry intelligence analysis; Wang Chao (corresponding author), Ph.D., associate professor, research direction in science and industry intelligence analysis, Email: kingtaoist@yeah.net; Liu Chunjiang, Ph.D., senior engineer, research direction in information technology and analysis; Zhang Huiling, master, librarian, research direction in scientometrics; Tan Xiao, Ph.D., associate researcher, research direction in scientometrics.
  • Supported by:
    This article is the outcome of the projects, "Early Recognition Method of Transformative Scientific and Technological Innovation Topics based on Weak Signal Temporal Network Evolution analysis"(72274113) supported by the National Natural Science Foundation of China, "Research on the Evolution Model and Mechanism of Weak Signals for Early Identification of Disruptive Technologies" supported by Shandong Provincial Social Science Foundation(23CTQJ07), Hubei Key Laboratory of Big Data in Science and Technology(E3KF291001).

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摘要： 本研究在领域主题粒度上探究学科交叉对科学突破的影响，推进对科学突破动力机制的认知。通过分析时序数据动态变化特征，揭示新兴研究主题的突破性潜力及其学科交叉特征。首先从新兴研究主题中识别科学突破主题，之后分析不同类别中科学突破主题的学科交叉特征，进而分析科学突破主题及其知识基础文献的被引量与学科交叉数量增减演进趋势的一致性，最后测度科学突破主题的被引量与其学科交叉数量时间序列之间的预测因果关系，以此探究科学突破的产生与学科交叉的关联。以干细胞领域为例开展实证研究发现，科学突破主题的学科交叉特征可划为三个类别，且科学突破主题与其知识基础在被引量和学科交叉数量的增减演进趋势上存在较高的一致性，大多科学突破主题被引量时间序列与学科交叉数量时间序列间的预测因果关系并不显著，因此不能仅用学科交叉数量指标识别或预判科学突破主题。本研究为更好地理解科学突破研究的特征提供了有用的线索与有益的启示。

关键词: 科学突破, 学科交叉, 引文曲线, 演化特征, 动力机制, 新兴研究主题

Abstract: The paper explores the impact of interdisciplinary collaboration on scientific breakthroughs at the granularity of research topics, advancing our understanding of the driving mechanisms behind scientific breakthroughs. By analyzing the dynamic characteristics of temporal data, it unveils the breakthrough potential of emerging research topics and their interdisciplinary features. Specifically this paper initially identifies scientific breakthrough topics based on emerging research topics and examines their interdisciplinary characteristics. Furthermore, the consistency in the increase and decrease evolutionary trends in the number of citations and the number of cross-disciplinary documents on scientific breakthrough topics and their knowledge base documents is analyzed. Finally, it measures the predictive causal relationship between the citation count of scientific breakthrough topics and their interdisciplinary quantity time series, thus investigating the association between the emergence of scientific breakthroughs and interdisciplinarity. Using stem cells research as a case study, this empirical research categorizes the interdisciplinary characteristics of scientific breakthrough topics into three categories. Results show that there is a great consistency between the scientific breakthrough topics and the knowledge base in the increase and decrease evolutionary trends in the number of citations and interdisciplinary quantity. However, the predictive causal relationship between the citation time series of most scientific breakthrough topics and the time series of interdisciplinary is not significant. Therefore, relying solely on the interdisciplinary quantity metric may not effectively identify or predict scientific breakthrough topics. The paper provides valuable insights to better understand the characteristics of scientific breakthrough research.

Key words: Scientific breakthrough, Interdisciplinary, Citation curve, Evolutionary characteristics, Dynamic mechanism, Emerging research topics

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