Tracing policy diffusion: Identifying main paths in policy citation networks

IF 1.8 4区 管理学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Zhichao Ba, Yao Tang, Xuetai Liu, Yikun Xia
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引用次数: 0

Abstract

Citation-based main path analysis (MPA) has been widely applied to identify developmental trajectories of science and technology, while rarely used to detect paths of policy diffusion. Compared with scientific publications and patents, policy documents show some distinct characteristics, such as citation relationships with different legal validity, which could be considered to improve the policy citation analysis. To this end, this study formally constructs a policy citation network based on a plethora of citing/cited links embedded in the textual content of policy documents and proposes a preference-adjusted main path analysis (PMPA) approach to track historical routes of policy diffusion. PMPA incorporates two kinds of policy citation preferences, including validity bias and time bias. An evidence analysis from China’s new energy policies (NEPs) is implemented to show the efficacy of the proposed approach. The results unveil that the preference-adjusted main path approach can capture more important policies and more informative main paths of policy diffusion than the original MPA. Moreover, our research can yield in-depth insight into the evolutionary process of policy diffusion and provide guidance for policy-makers and industry decision-makers to formulate practical policy-making.
追踪政策扩散:确定政策引用网络中的主要路径
基于引文的主路径分析(MPA)被广泛应用于科学技术发展轨迹的识别,但很少用于政策扩散路径的检测。与科技出版物和专利相比,政策文献表现出不同法律效力的引文关系等明显特征,可以考虑完善政策文献的引文分析。为此,本研究基于嵌入在政策文件文本内容中的大量被引/被引链接,正式构建了政策引用网络,并提出了偏好调整的主路径分析(PMPA)方法来追踪政策扩散的历史路径。PMPA包含两种政策引用偏好,包括效度偏倚和时间偏倚。通过对中国新能源政策(NEPs)的实证分析,证明了该方法的有效性。结果表明,与原始MPA相比,偏好调整的主要路径方法可以捕获更多重要的政策和更具信息量的政策扩散主要路径。此外,我们的研究可以深入了解政策扩散的演化过程,为政策制定者和行业决策者制定切合实际的政策提供指导。
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 Information Science
Journal of Information Science 工程技术-计算机:信息系统
CiteScore
6.80
自引率
8.30%
发文量
121
审稿时长
4 months
期刊介绍: The Journal of Information Science is a peer-reviewed international journal of high repute covering topics of interest to all those researching and working in the sciences of information and knowledge management. The Editors welcome material on any aspect of information science theory, policy, application or practice that will advance thinking in the field.
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