| 广东省科技人才竞争力评价与提升策略 |
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| 引用本文： | 陈杰,刘佐菁.广东省科技人才竞争力评价与提升策略[J].科技管理研究,2018(22). |
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| 作者姓名： | 陈杰 刘佐菁 |
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| 作者单位： | 广东省科技创新监测研究中心,广东省科技创新监测研究中心 |
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| 基金项目： | 广东省科技计划项目“广东省重大科技人才工程实施支撑机构能力提升建设”(2016B070713001) |
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| 摘 要： | 结合我国科技人才发展实际,从科技人才规模、结构、投入、产出和环境5个维度出发,构建我国科技人才竞争力评价指标体系,在此基础上,利用主成分分析法对我国31个省市2011—2015年科技人才竞争力进行评价与比较,发现广东省科技人才竞争力总体优势明显,居全国第三,且近年来综合竞争力在各省市中有缓慢上升的趋势。从各主成分看,广东省科技人才投入竞争力和人才环境竞争力排名靠前,但科技人才资源竞争力明显不强,与北京、江苏等省市相比还有较大差距,为此,提出进一步提升广东科技人才竞争力的对策。
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| 关 键 词： | 科技人才 人才竞争力 评价体系 主成分分析 |
| 收稿时间： | 2018/2/2 0:00:00 |
| 修稿时间： | 2018/3/5 0:00:00 |
Evaluation and Promotion Strategies of Competitiveness of Science and Technology Talents in Guangdong Province |
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| Abstract: | Talents are important support driven by innovation. According to the actual development of science and technology talents in our country, we select 20 indicators from the five dimensions of science and technology personnel scale, structure, input, output and environment, and build the evaluation index system of competitiveness of science and technology talents in our country. On the use of principal component analysis of China''s 31 provinces and cities in 2011-2015 to evaluate the competitiveness of science and technology talent and found that the overall competitiveness of scientific and technological talents in Guangdong Province overall obvious advantages, ranking third in the country, and in recent years the comprehensive competitiveness in all provinces The city has slowly increased trend. From the perspective of each principal component, Guangdong Province ranks the top with the competitiveness of scientific and technological talents and the competitiveness of human resources. However, the competitiveness of science and technology talent resources is obviously not strong, and there is still a big gap compared with Beijing and Jiangsu provinces. For this reason, Proposed to further enhance the competitiveness of science and technology talents in Guangdong countermeasures. |
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| Keywords: | Science and Technology Talent talent competitiveness evaluation system principal component analysis |
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