JCR의 영향력지수 순위를 활용한 문헌정보학 분야 학술지 평가의 한계
Limitations in Using JCR Impact Factor Rankings for LIS Journals
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초록
최근 문헌정보학 연구자의 연구업적 평가에 JCR의 도서관학․정보학 카테고리 분류가 영향을 주고 있어 주목을 끈다. 도서관학․정보학 핵심 학술지에 출판하면 영향력지수 순위에서 밀려 좋은 평가를 받기 어렵고, 도서관학․정보학 카테고리 내에서 영향력지수 상위권인 MIS 학술지에 출판하면 우수하다는 평가를 받는다는 것이다. 이에 이 연구는 JCR의 도서관학․정보학 카테고리에 속한 학술지의 세부 주제영역을 규명한 선행연구를 토대로, 학술지 저자분석을 통해 문헌정보학과 MIS의 교차 상황을 분석하였다. 분석결과 문헌정보학과 MIS 간의 교차점은 크지 않았고, 연결강도도 강하지 않았다. 또한 서울소재 문헌정보학과 개설 대학의 약 55%가 JCR의 주제 카테고리별 학술지의 영향력지수 순위를 구성원의 연구업적 평가에 직접 반영하고 있었다. 이로써 JCR의 주제 카테고리 분류가 연구자의 연구업적평가에 부정적인 영향을 줄 수 있고, 학술 출판을 왜곡시킬 가능성이 있음을 알게 되었다. 이와 같은 연구업적평가 기준의 문제점은 시급히 보완되고 개선되어야 할 것이며, 이 연구결과는 이러한 정책 개선의 기초자료로 활용될 수 있을 것으로 기대된다.
Abstract
JCR’s categorization significantly impacts the evaluation of research achievements in the field, particularly in information science and library science (IS & LS). Publishing in prestigious IS & LS journals often leads to lower evaluations due to their lower impact factor rankings. In contrast, publishing in higher-ranked Management Information Systems (MIS) journals classified under the IS & LS category is evaluated more favorably. Accordingly, this study analyzed the intersection of Library and Information Science (LIS) and MIS through an analysis of journal authors. This analysis was based on previous research that identified detailed subject areas of academic journals categorized under the IS & LS category in the JCR. As a result of the analysis, the intersection between the subfields of LIS and MIS was not substantial, and the strength of the connection was weak. Additionally, approximately 55% of universities in Seoul with LIS departments use JCR’s journal rankings by subject category to assess research achievements. The findings suggest that JCR’s subject category classification may negatively influence researchers’ evaluations and potentially distort the academic publishing landscape. In the future, the issues with research achievement evaluation standards will need to be supplemented and improved. The results of this study are expected to serve as valuable fundamental data for policy improvement.
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