周学军, 郑雅雯. 江西省科技人才社会生态环境评价研究[J]. 南昌航空大学学报(社会科学版), 2016, 18(3): 61-67. DOI: 10.3969/j.issn.1009-1912.2016.03.010
引用本文: 周学军, 郑雅雯. 江西省科技人才社会生态环境评价研究[J]. 南昌航空大学学报(社会科学版), 2016, 18(3): 61-67. DOI: 10.3969/j.issn.1009-1912.2016.03.010
ZHOU Xue-jun, ZHENG Ya-wen. Study on the Evaluation System of Jiangxi Science and Technology Talents' Social Eco-environment[J]. JOURNAL OF NANCHANG HANGKONG UNIVERSITY(SOCLAL SCIENCES), 2016, 18(3): 61-67. DOI: 10.3969/j.issn.1009-1912.2016.03.010
Citation: ZHOU Xue-jun, ZHENG Ya-wen. Study on the Evaluation System of Jiangxi Science and Technology Talents' Social Eco-environment[J]. JOURNAL OF NANCHANG HANGKONG UNIVERSITY(SOCLAL SCIENCES), 2016, 18(3): 61-67. DOI: 10.3969/j.issn.1009-1912.2016.03.010

江西省科技人才社会生态环境评价研究

Study on the Evaluation System of Jiangxi Science and Technology Talents' Social Eco-environment

  • 摘要: 科技人才社会生态环境建设是江西省实现人才集聚及创新驱动的关键因素。江西省11个城市的科技人才社会生态环境评价指标体系可以从经济基础、科技发展、人才保障三方面构建。采用因子分析法提取出的科技人才社会生态环境指标体系的二维主成份,揭示了科技人才社会生态环境体系的结构不平衡问题。要解决这些问题,在参照经济因子和生活因子得分的基础上,结合聚类分析做出的江西省三类城市划分视角,得到江西省科技领先城市、科技中等城市、科技滞后城市的科技人才社会生态环境发展的关键因素。

     

    Abstract: The construction of science and technology talents' social eco-environment is one of the necessary elements to achieve the Jiangxi province's talent aggregation and innovative development. With the data from 11 cities of Jiangxi, the evaluation system of science and technology talents' social eco-environment has been built through the economic foundation, technological development, and talent guaranteeing. This article applied the factor-analytic approaches to distinguish the economic factor and leisure factor from Jiangxi science and technology talents' social eco-environment, and found the problem of unbalanced structure. For solving this problem, we respectively put up some key elements and viable suggestion for the advanced-level city of Science and technology, the medium-level city of science and technology, and the lag-level city of science and technology on the base of the results of the clustering method's analysis and the principal component scores.

     

/

返回文章
返回