The initiative of refining the CAS journal subject classification system - Liao, Yu; Zhang, Jiandong; Yang, Liying; Shen, Zhesi (Journal of data and information science (Warsaw, Poland), 2025)
The need to refine journal subject classification systems Journal subject classification systems are fundamental to journal evaluation, research assessments and information retrieval. Inaccurate journal classification systems can lead to several problems such as: ➢ Possible evaluation bias, e.g., the under-representation of physical chemistry journals (Shen et al., 2024), and the suppression of LIS Journals in the JCR Information Science and Library Science category (Huang et al., 2019); ➢ Missing relevant journals in competitive analysis for journal strategic planning; ➢ Inaccurate assessments of journal disciplinary positioning leading to misguided policies (Zhang et al., 2024); ➢ Information retrieval issues (both over-inclusion and omission), affecting subsequent analyses. Goals and methodology In this initiative, we propose a hybrid approach combining quantitative and qualitative methods to refine the journal subject category and form the correspondence between subject category and paper-level topics.