基于ESI和InCites数据的高校学科集群与优势学科探测研究——以我国36所A类“双一流”建设高校为例

Research on the Discipline Cluster and Advantageous Discipline Detection of Universities Based on Databases of ESI and InCites: Case study of 36 “Double First Class” Universities

[目的/意义]旨在为高校学科发展和学科布局调整提供理论依据和数据支撑。[方法/过程]基于技术距离等相关理论,探索性地提出学科相对优势指数和学科结构相似度指数,以36所A类"双一流"建设高校为例,采用2018年5月ESI和InCites学科数据,通过多维尺度分析和K-means聚类方法对高校的学科集群和相对优势学科进行探测研究。[结果/结论]研究发现:学科相对优势指数和学科结构相似度指数能够客观分析高校学科发展状况;根据学科结构相似度指数,"双一流"建设高校被划分为6个学科集群大类,75%的高校聚集在二维坐标图的原点附近,学科结构相似度较高; 36所高校的学科相对优势指数在0.002～18. 575之间,学科差异度较大,表现出非均衡发展的特点。

[Purpose/significance] The paper is to provide theoretical basis and data support for the development and adjustment of subject layout.[Method/process] Based on the technical distance theory,the paper explores?to?put forward the revealed disciplinary advantage index (RDA) and degree of discipline similarity index (DDS).By the K-means clustering and multidimensional scale analysis,the discipline cluster and advantageous discipline are detected in 36"Double First Class" universities based on ESI and InCites databases of May 2018.[Result/conclusion] The result shows that indexes of RDA and DDS can objectively analyze the subject development of universities.According to DDS index,the"Double First Class"universities are divided into six subject clusters.The 75%of universities gather near the origin of the two-dimensional coordinate chart which suggests that these universities have high degrees of similarity in subject structure.The RDA index of 36 universities are between 0.002~18.575.These disciplines of universities vary significantly which show the characteristics of unbalanced development.