바로가기메뉴

본문 바로가기 주메뉴 바로가기

logo

Scopus에 설정된 주제분류 활용도 및 상호 연관성에 대한 고찰

Assessing the Utilization and Interrelatedness of Scopus Subject Categories

한국도서관·정보학회지 / Journal of Korean Library and Information Science Society, (P)2466-2542;
2019, v.50 no.1, pp.251-272
https://doi.org/10.16981/kliss.50.1.201903.251
Kim, Eungi (계명대학교)
  • 다운로드 수
  • 조회수

Abstract

This study investigated the utilization and interrelatedness of Scopus subject categories. To conduct this study, major and minor subject categories of journals listed in the 2017 Scopus index were used. The results showed varying degrees of interrelatedness of subject categories. At the major subject category level, the utilization was the highest in Medicine, while Social Sciences showed a greater degree of interrelatedness in comparison to Medicine. Yet, at the minor subject level, 2700 General Medicine was particularly dominant in terms of utilization and interrelatedness. Moreover, co-occurrences of minor subject categories showed varying degrees of interrelatedness between pairs of minor subject categories. Pairs of minor subject categories showed the following characteristics: a) two subject categories having identical or closely identical descriptions, b) two different categories having an interrelationship by subject areas, and c) one category conceptually encompassing another category. Due to varying degrees of utilization and interrelatedness among subject categories, minor subject categories that may greatly influence the major subject categories in conducting research studies should be investigated in detail.

keywords
ASJC 코드, 주제분류, Scopus, 활용도, 상호 관련성, ASJC codes, Subject categories, Scopus, Utilization, Interrelatedness

참고문헌

1.

Abrizah, Abdullah et al. 2013. “LIS Journals Scientific Impact and Subject Categorization: A Comparison Between Web of Science and Scopus.” Scientometrics, 94(2): 721-740.

2.

Aho, Alfred V., Brian W. Kernighan and Peter J. Weinberger. 1979. “Awk a Pattern Scanning — and Processing Language.” Software: Practice and Experience, 9(4): 267-279.

3.

Batagelj, Vladimir and Andrej Mrvar, A. 1998. “Pajek-Program for Large Network Analysis.” Connections, 21(2): 47-57.

4.

Bervkens, Peter. 2012. “SciVerse Scopus Custom Data Documentation.” Available from: http://ebrp.elsevier.com/pdf/Scopus_Custom_Data_Documentation_v4.pdf

5.

Chung, Jenny and Ming-Yueh Tsay. 2017. “A Bibliometric Analysis of the Literature on Open Access in Scopus.” Qualitative and Quantitative Methods in Libraries, 4(4): 821-841.

6.

Dorta-González, Pablo and Yolanda. Santana-Jiménez. 2017. “Prevalence and Citation Advantage of Gold Open Access in The Subject Areas of the Scopus Database.” Research Evaluation, 27(1): 1-15.

7.

Freeman, Linton. C. 1978. “Centrality in Social Networks Conceptual Clarification.” Social Networks, 1(3): 215-239.

8.

García, Jose A., Rosa Rodriguez Sánchez and J. Fdez Valdivia. 2011. “Ranking of the Subject Areas of Scopus.” Journal of the American Society for Information Science and Technology, 62(10): 2013-2023.

9.

Gómez-Núñez, Antonio J. et al. 2011. “Improving SCImago Journal and Country Rank (SJR) Subject Classification Through Reference Analysis.” Scientometrics, 89(3): 741.

10.

Hassan, Saeed-Ul et al. 2017. “Measuring Social Media Activity of Scientific Literature: An Exhaustive Comparison of Scopus and Novel Altmetrics Big Data.” Scientometrics, 113(2): 1037-1057.

11.

Kim, Jinkwang, Sohyung Kim and Changhyuck Oh. 2016. “A Classification of the Journals in KCI Using Network Clustering Methods.” Journal of the Korean Data and Information Science Society, 27(4): 947-957.

12.

Klarenbeek, Tracy and Nelius Boshoff. 2018. “Measuring Multidisciplinary Health Research at South African Universities: A Comparative Analysis Based on Co-Authorships and Journal Subject Categories. Scientometrics, 116(3), 1461-1485.

13.

Kosecki, Stanislaw, Robin Shoemaker and Charlotte Kirk Baer. 2011. “Scope, Characteristics, and Use of the US Department of Agriculture's Intramural Research.” Scientometrics, 88(3): 707-728.

14.

Lee, Jaeyoon. 2006. “Centrality Measures for Bibliometric Network Analysis.” Journal of the Korean Society for Library and Information Science, 40(3): 191-214.

15.

Leydesdorff, Loet. 2007. “Betweenness Centrality as an Indicator of the Interdisciplinarity of Scientific Journals.” Journal of the American Society for Information Science and Technology, 58(9), 1303-1319.

16.

Leydesdorff, Loet. and Ismael Rafols. 2009. “A Global Map of Science Based on the ISI Subject Categories.” Journal of the American Society for Information Science and Technology, 60(2), 348-362.

17.

Leydesdorff, Loet and Lutz Bornmann. 2016. “The Operationalization of Fields as WoS Subject Categories (WCS) in Evaluative Bibliometrics: The Cases of Library and Information Science and Science and Technology Studies.” Journal of the Association for Information Science and Technology, 67(3): 707-714.

18.

Martín-Martín, Alberto et al. 2018. “Google Scholar, Web of Science, and Scopus: A Systematic Comparison of Citations in 252 Subject Categories.” Journal of Informetrics, 12(4), 1160-1177.

19.

Minguet, Fernando et al. 2017. “Redefining the Pharmacology and Pharmacy Subject Category in the Journal Citation Reports Using Medical Subject Headings (Mesh).” International Journal of Clinical Pharmacy, 39(5): 989-997.

20.

Newman, Mark EJ. 2005. “A Measure of Betweenness Centrality Based on Random Walks.” Social Networks, 27(1): 39-54.

21.

Porter, Alan et al. 2007. “Measuring Researcher Interdisciplinarity.” Scientometrics, 72(1): 117-147.

22.

Ronda-Pupo, Guillermo Armando, Yesenia Ronda-Danta and Yusleydis Leyva-Pupo. 2016. “Correlation Between a Country's Centrality Measures and the Impact of Research Paper: The Case of Biotechnology Research in Latin America.” Investigación Bibliotecológica: Archivonomía, Bibliotecología e Información, 30(69): 73-92.

23.

Stanton, Colleen Maura. 2014. A Health Promoting Continuous Learning Sustainable Education System. PhD Thesis. University of Toronto, Canada.

24.

Thelwall, Mike. 2017. “Are Mendeley Reader Counts High Enough for Research Evaluations When Articles Are Published?” Aslib Journal of Information Management, 69(2): 174-183.

25.

Valente, Thomas W. et al. 2008. “How Correlated Are Network Centrality Measures?” Connections (Toronto, Ont.), 28(1): 16.

26.

Van Eck, Nees and Ludo Waltman. 2009. “Software Survey: Vosviewer, A Computer Program for Bibliometric Mapping.” Scientometrics, 84(2), 523-538.

27.

Waltman, Ludo, Nees Jan Van Eck and Ed CM Noyons. 2010. “A Unified Approach to Mapping and Clustering of Bibliometric Networks.” Journal of Informetrics, 4(4): 629-635.

28.

Wagner, Caroline S. et al. 2011. “Approaches to Understanding and Measuring Interdisciplinary Scientific Research (IDR): A Review of the Literature.” Journal of informetrics, 5(1): 14-26.

29.

Wang, Qi and Ludo Waltman. 2016. “Large-Scale Analysis of the Accuracy of the Journal Classification Systems of Web of Science and Scopus.” Journal of Informetrics, 10(2): 347-364.

30.

Yan, Erjia. 2016. “Disciplinary Knowledge Production and Diffusion in Science.” Journal of the Association for Information Science and Technology, 67(9): 2223-2245.

31.

Zuccala, Alesia and Raf Guns. 2013. “Comparing Book Citations in Humanities Journals to Library Holdings: Scholarly Use Versus Perceived Cultural Benefit.” 14th International Conference of the International Society for Scientometrics and Informetrics, 1: 353-360.

한국도서관·정보학회지