Measuring the efficiency of highly funded scientists in China based on the data envelopment analysis

Keyu Xiang, Haiming Liang, Zhaoxia Guo, Yucheng Dong
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Abstract

Since the competition in the scientific community is becoming increasingly fierce, measuring the research performance of highly funded scientists is growingly critical for relevant management authorities to make effective policies in scientific research and development. However, little quantitative analysis has focused on this issue. To facilitate the research in this area, we first construct a dataset containing both the funding inputs and the research paper outputs of highly funded scientists in China. Then, instead of directly summing quantitative indicators, in this paper, we measure the efficiency of highly funded scientists in China based on the data envelopment analysis models. We show that we can grade highly funded scientists into several grades and present their scores on attractiveness or progress to other grades. The gradual learning path for less efficient scientists is also provided. These patterns of highly funded scientists may be beneficial for management authorities to make effective policies.
基于数据包络分析的中国高资助科学家效率测度
由于科学界的竞争日益激烈,衡量高资助科学家的研究绩效对于相关管理部门制定有效的科学研究与发展政策变得越来越重要。然而,很少有定量分析关注这一问题。为了促进这一领域的研究,我们首先构建了一个包含中国高资助科学家的资金投入和研究论文产出的数据集。然后,本文采用数据包络分析模型对中国高资助科学家的效率进行测度,而不是直接对量化指标进行求和。我们表明,我们可以将获得高额资助的科学家分为几个等级,并将他们的吸引力或进步分数呈现给其他等级。为效率较低的科学家提供了渐进式学习路径。这些高资助科学家的模式可能有利于管理当局制定有效的政策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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