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

Elio Atenógenes Villaseñor (), Ricardo Arencibia-Jorge () and Humberto Carrillo-Calvet ()
Additional contact information
Elio Atenógenes Villaseñor: Center of Research and Innovation in Information and Communication Technologies INFOTEC
Ricardo Arencibia-Jorge: Empresa de Tecnologías de la Información
Humberto Carrillo-Calvet: National Autonomous University of Mexico

Scientometrics, 2017, vol. 110, issue 1, No 5, 77-104

Abstract: Abstract 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.

Keywords: Bibliometric rankings; Higher education; Institutional academic assessment; Scientometric indicators; Self-organized neural networks; Scientometric data mining; Mexico; 68T10; 62H30; 91C20; C630; I230 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-016-2166-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:110:y:2017:i:1:d:10.1007_s11192-016-2166-0

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-016-2166-0

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().