Multiparametric characterization of scientometric performance profiles assisted by neural networks: a study of Mexican higher education institutions

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• 作者：Elio Atenógenes Villaseñor ; Ricardo Arencibia-Jorge…
• 关键词：Bibliometric rankings ; Higher education ; Institutional academic assessment ; Scientometric indicators ; Self ; organized neural networks ; Scientometric data mining ; Mexico
• 刊名：Scientometrics
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：110
• 期：1
• 页码：77-104
• 全文大小：
• 刊物主题：Information Storage and Retrieval; Library Science;
• 出版者：Springer Netherlands
• ISSN：1588-2861
• 卷排序：110

文摘

Development of accurate systems to assess academic research performance is an essential topic in national science agendas around the world. Providing quantitative elements such as scientometric rankings and indicators have contributed to measure prestige and excellence of universities, but more sophisticated computational tools are seldom exploited. We compare the evolution of Mexican scientific production in Scopus and the Web of Science as well as Mexico’s scientific productivity in relation to the growth of the National Researchers System of Mexico is analyzed. As a main analysis tool we introduce an artificial intelligence procedure based on self-organizing neural networks. The neural network technique proves to be a worthy scientometric data mining and visualization tool which automatically carries out multiparametric scientometric characterizations of the production profiles of the 50 most productive Mexican Higher Education Institutions (in Scopus database). With this procedure we automatically identify and visually depict clusters of institutions that share similar bibliometric profiles in bidimensional maps. Four perspectives were represented in scientometric maps: productivity, impact, expected visibility and excellence. Since each cluster of institutions represents a bibliometric pattern of institutional performance, the neural network helps locate various bibliometric profiles of academic production, and the identification of groups of institutions which have similar patterns of performance. Also, scientometric maps allow for the identification of atypical behaviors (outliers) which are difficult to identify with classical tools, since they outstand not because of a disparate value in just one variable, but due to an uncommon combination of a set of indicators values.