Dominant Characteristics of Subject Categories in a Multiple-Category Hierarchical Scheme: A Case Study of Scopus

Author:

Kim Eungi1ORCID,Jeong Da-Yeong1ORCID

Affiliation:

1. Department of Library and Information Science, Keimyung University, 1095 Dalgubeoldaero, Dalseo-Gu, Daegu 42601, Republic of Korea

Abstract

The Scopus journal classification method, known as All Science Journal Classification (ASJC), follows a hierarchical organization of subject categories: minor, major, and supergroups. At the minor level, journals are assigned to one or more subject categories. We refer to this classification scheme as a multiple-category hierarchical scheme. The objective of this study is to investigate the dominant characteristics of subject categories within the Scopus database and quantify their dominance using various subject indices. To conduct the study, we formulated a set of subject category indices, including the Number of Journals (J), Total Instances of Subject Categories (SC), Number of Unique Subject Categories (USC), and Dominance Index (DOMI). The results showed that high DOMI values in subject categories indicate specialization and limited associations with other fields. There were minimal correlations between DOMI and other subject category indices like J, SC, and USC, demonstrating their uniqueness and independence. The study also revealed that subject categories within the Health Sciences exhibited higher DOMI values and greater specialization compared to those in the Physical Sciences, indicating a pronounced dominance in Health Sciences minor categories. Finally, minor subject categories exhibited more variation in subject category indices compared to their upper-level subject categories, highlighting the intricate variations within the hierarchical system of the Scopus classification. These findings have implications for researchers, emphasizing the need to consider a subject category’s dominance and associations when selecting journals for their research.

Publisher

MDPI AG

Subject

Computer Science Applications,Media Technology,Communication,Business and International Management,Library and Information Sciences

Reference22 articles.

1. Chen, L.X., Wong, K.S., Liao, C.H., and Yuan, S.M. (2020, January 21–23). Predatory journal classification using machine learning. Proceedings of the 2020 3rd IEEE International Conference on Knowledge Innovation and Invention (ICKII), Kaohsiung, Taiwan.

2. A new classification scheme of science fields and subfields designed for scientometric evaluation purposes;Schubert;Scientometrics,2003

3. Categorizing journals using Scopus subject areas: A comparison of Chinese and international journals;Niu;Scientometrics,2017

4. (2022, June 30). Elsevier: What is the complete list of Scopus Subject Areas and All Science Journal Classification Codes (ASJC)?. Available online: https://service.elsevier.com/app/answers/detail/a_id/15181/supporthub/scopus/.

5. Ranking of the subject areas of Scopus;J. Am. Soc. Inf. Sci. Technol.,2011

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