Elsevier's journal metrics for the identification of a mainstream journals core : a case study on Mexico

• Main Authors: Arencibia-Jorge, Ricardo, Villaseñor, Elio Atenógenes, Lozano-Díaz, Ibis A., Calvet, Humberto Carrillo
• Format: Article
• Language: English
• Published: 2021
• Subjects: Library and information science
• Online Access: https://hdl.handle.net/10356/154311
• Institution: Nanyang Technological University

Background. Over the past four decades there have been considerable arguments against the arbitrary use of Garfield’s Impact Factor in research evaluation. Over the past decade, the emergence of databases with considerable breadth of coverage, such as Scopus and Google Scholar, together with the general demand for greater accuracy in journal assessments, has motivated the emergence of alternative journal indicators. However, the study of Latin American journals using these quantitative measures is still scarce. Objectives. The purpose of this paper is to highlight the use of advanced bibliometric indicators to analyze the performance of scientific journals, and to identify a mainstream journals core in national and regional contexts. Methods. Thomson Reuters’ Journal Citation Report and Elsevier’s Journal Metrics were used as information sources. Four bibliometric indicators were selected to identify clusters of high-visibility journals: Impact Factor, Eigenfactor Score, Source Normalized Impact per Paper, and SCImago Journal Rank. A Self-Organizing Map (SOM) based artificial intelligence method to identify a mainstream journals core was also used. Mexican serials were selected for the case study. Results. The coverage of Mexican journals in Web of Science and Scopus during the period 2004-2013 was studied. The performance of Mexican scientific journals was also evaluated. Mexican journal rankings based on Scopus-based metrics versus Web of Science-based metrics were compared. Contributions. The most visible Mexican mainstream serials core is identified. Librarians, researchers and research managers can use alternative indicators and Self-Organizing Maps to characterize journals rankings.