社会科学数据集的跨学科性研究 ——以CHARLS和CGSS数据集为例  

Interdisciplinary Research on Social Science Datasets——A Case Study of the CHARLS and CGSS Datasets

作　　者：刘智锋 王继民[1] Liu Zhifeng;Wang Jimin(Department of Information Management,Peking University,Beijing 100871,China)

机构地区：[1]北京大学信息管理系,北京100871

出　　处：《现代情报》2023年第9期165-177,共13页Journal of Modern Information

基　　金：国家社会科学基金重点项目“开放科学数据集统一发现的关键问题与平台构建研究”(项目编号:20ATQ007)。

摘　　要：[目的/意义]分析社会科学数据集的跨学科性有助于理解数据集在不同学科的扩散规律,促进数据集在不同学科之间开放共享。[方法/过程]本文以CHARLS和CGSS数据集为例,首先对CHARLS和CGSS数据集的学科多样性与平衡性进行测度分析;其次构建CHARLS和CGSS数据集跨学科合作网络,采用Louvain算法对网络进行聚类,探测不同的研究社区,然后采用BERTopic对使用数据集的文本进行主题建模;最后构建不同阶段的跨学科合作网络,揭示CHARLS和CGSS数据集跨学科合作演化特征。[结果/结论]CHARLS和CGSS数据集的跨学科多样性和平衡性不断增长,使用CHARLS和CGSS数据集的学科均形成了以少数学科为主导,多学科共同参与的格局;使用CGSS数据集的研究主题比CHARLS数据集的相对较为分散;CHARLS和CGSS数据集的跨学科合作网络节点数、边数以及社区数在不断增长,网络密度有所下降,不同阶段的主导学科不断变化。[Purpose/Significance]Analyzing the interdisciplinary nature of social science datasets helps to understand their diffusion patterns across different disciplines and promote open sharing among them.[Method/Process]Used CHARLS and CGSS datasets as examples,this paper first measured the disciplinary diversity and balance of the two datasets.Then,constructed a cross-disciplinary collaboration network for CHARLS and CGSS datasets,and used the Louvain algorithm to cluster the network and detect different research communities.Next,used BERTopic to model the topics of the papers using the datasets.Finally,constructed cross-disciplinary collaboration networks at different stages to reveal the evolutionary characteristics of cross-disciplinary collaboration for CHARLS and CGSS datasets.[Results/Conclusion]The cross-disciplinary diversity and balance of CHARLS and CGSS datasets have continued to grow,and the disciplines that use CHARLS and CGSS datasets have formed a pattern in which a few disciplines dominate while multiple disciplines participate together.The research topics using the CGSS dataset are relatively more scattered than those using the CHARLS dataset.The number of nodes,edges,and communities in the cross-disciplinary collaboration networks of CHARLS and CGSS datasets has been continuously increasing,while the network density has decreased.Additionally,the dominant disciplines in different stages of cross-disciplinary collaboration have been constantly changing.

关 键 词：社会科学 数据集 跨学科 CHARLS CGSS 

分 类 号：G203[文化科学—传播学]