Construction and Deconstruction:What Capabilities Are Required to Become a Leading Scientific and Technological Talent? — A Study Based on Qualitative Coding and Social Network Analysis
Zhang Yanglei, Xu Yusheng
School of Education,Huazhong University of Science and Technology,Wuhan 430074,China
Abstract：Cultivating world-class leading scientific and technological talents is a top priority for strengthening the nation's strategic talent capacity.Clarifying the core competencies of such talents forms the educational foundation for their development.Drawing on in-depth interview data from 34 leading scientific and technological talents,including academicians of the Chinese Academy of Sciences and the Chinese Academy of Engineering,this study constructs a framework of core competencies using qualitative coding and conducts quantitative analysis through social network analysis.The results reveal a core competency framework comprising 34 elements forming a highly interconnected network.Within this network,continuous learning and knowledge updating,profound professional expertise,and diligence occupy dominant positions.The overall structure exhibits a hierarchical pattern of“core-semi-core-peripheral”competencies.Further analysis identifies four interrelated sub-clusters within the network:academic interest and aspiration,striving spirit and leadership,integration and adaptability,and moral-psychological foundations.Based on these findings,the study suggests that programs for cultivating top scientific and innovative talents should emphasize the core competencies occupying key network positions,create collaborative conditions that foster the emergence of competency clusters,and strengthen peripheral competencies through practical engagement.
张洋磊, 徐玉胜. 建构与解构:成为科技领军人才需要具备何种能力？——基于质性编码与社会网络分析的研究[J]. 中国科技论坛, 2026(7): 138-148.
Zhang Yanglei, Xu Yusheng. Construction and Deconstruction:What Capabilities Are Required to Become a Leading Scientific and Technological Talent? — A Study Based on Qualitative Coding and Social Network Analysis. , 2026(7): 138-148.
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