Coding Scheme for Profiles of Distinguished Sci-Tech Talents (Partial Sample, December 2024)

This dataset contains structured, coded multi-dimensional characteristic data for 847 distinguished sci-tech talents in China (partial sample). Through systematic coding, it associates a talent's election age (dependent variable) with their cultural capital, social capital, and symbolic capital (core independent variables), as well as control variables including gender, generational cohort, birth region, political status, and research field. The coding is derived from publicly available resumes, award records, and academic biographical information, following a clear set of classification and quantification rules (e.g., a 3-tier classification for institutional prestige, a 5-level scoring for awards, and a 4-segment division for generational cohorts). This dataset is suitable for quantitative research and heterogeneity analysis in areas such as the growth patterns of sci-tech talents, research evaluation systems, and the effectiveness of high-level talent policies.

本数据集包含847名中国杰出科技人才的结构化编码多维特征数据（抽样样本）。通过系统化编码，本数据集将人才的获评年龄（因变量）与文化资本、社会资本、象征资本（核心自变量），以及性别、世代群体、出生地、政治面貌、研究领域等控制变量进行关联。该编码工作基于公开可获取的简历、获奖记录与学术生平信息开展，遵循一套明确的分类与量化规则：例如对机构声望采用三级分类、对奖项采用五级计分、对世代群体采用四段划分。本数据集适用于科技人才成长规律、科研评价体系、高层次人才政策效能等领域的定量研究与异质性分析。