基金项目学部分部的交叉网络分析——以美国NSF数据中AI领域为例

Network Analysis of Inter-Division in Funding Projects:Case Studies of AI Field in NSF Data

本文旨在通过探究基金资助项目研究的交叉融合状况与趋势,从学部分部内容交叉层面探究基金投入对交叉研究的引导方向、影响特征及演化作用。本文组合共词网络分析和学科交叉研究等方法,从基金学部分部内在知识的聚合和基金分部间知识的交叉两个维度,构建对分部内部知识发展状况及分部间交叉态势的分析框架和测度方法。研究结果表明,以美国NSF (National Science Foundation)数据中AI (artificial intelligence)领域为例,该领域在基金投入引导下,随着年代递进,不同分部受资助项目数量增加,主题多样性增加,分部内部知识聚合程度降低,分部间交叉融合程度增强,同时分部内部知识聚合程度和分部间交叉融合程度均出现了明显分化;NSF分部的知识交叉集中在有相同或相似的理论方法的学科框架之下,表明知识实现近距离交叉融合更容易;内部知识的聚合性与外部知识的交叉程度具有一定的关联性。

This study aims to explore the funding direction,influence,characters,and evolution of a crossing study from the perspective of division by analyzing the research fusion and trends of funding projects.A co-word network analysis is combined with interdisciplinary research methods to construct an analysis framework and a measurement principle with an aim to discover the development of the inner-division knowledge and the inter-division research trends from the dimensions of the inner-division compactness of co-word networks and the intersection of inter-division knowledge.The results show that,over time,as per the National Science Foundation(NSF) data,both the number of projects and the diversity of topics in the different divisions of the artificial intelligence(AI) field are increasing.Further,from the two dimensions,the degree of inner-division compactness keeps decreasing while the intersection of inter-division knowledge intensifies,and there exists an obvious polarization between them.Moreover,the intersecting content of NSF’s different divisions is concentrated in the topics with the same or similar theoretical methods,which indicates that it is easier to realize cross-integration for similar knowledge,and there exists some relationship between the inner-division compactness of co-word networks and the crossing and integration of inter-division knowledge.