更复杂更高效:基于研究出版物的顶尖大学特征

Jiaxing Li, Luna Wang, Yiming Sun, Guojiang Shen, Ivan Lee, Xiangjie Kong
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引用次数: 0

摘要

探索新的科学概念和传授知识是大学的重要作用。目前,关于机构科研产出的信息管理研究主要集中在论文的数量和质量上。本文从产出和复杂性的角度提出了一种新的表征方法来提取学术信息。排名靠前的大学通过研究产出和复杂性来确定不同的表现。不同大学的产出指标是根据每所大学每年产出的研究论文数量来计算的。不同大学的复杂性指标是根据不同研究学科的加权显示比较优势得出的。通过使用同时考虑四个指标的无监督竞争学习算法,我们构建了一个连贯的框架来抓住大学研究产出的本质。研究发现,高校科研复杂性与科研产出之间存在正相关关系,不同的高校集群之间的正相关关系的上升速度不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
More Complex More Productive: Characterizing Top Universities Based on Research Publications
Exploring new scientific concepts and imparting knowledge are important roles of universities. Up to now, most information management study on institutional research output focuses on the number and excellence of paper. This paper proposes a new characterization method from the perspective of output and complexity to extract academic information. Top-ranked universities are selected to identify different performance through research production and complexity. The production indicator of different universities is calculated based on the annual number of research paper produced in each university. The complexity indicator of different universities is obtained according to weighted revealed comparative advantage over different research disciplines. By using an unsupervised competitive learning algorithm that considers four indicators simultaneously, we construct a coherent framework to seize the nature of universities' research output. As a key finding, we discover that university research complexity has a positive relationship with research production and a different cluster of universities has a different rate of rising of the positive relationship.
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