arrow
Return

Analyzing journal category assignment using a paper-level classification system: multidisciplinary sciences journals

delete2024-01-13
delete4
PRE
AI
张健东 (Jiandong Zhang)
Z
Zhesi Shen *
DOI:10.1007/s11192-023-04913-0delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
Accurate identification and classification of multidisciplinary journals is crucial in scientometrics for revealing scientific structure and evaluating journals. Using Web of Science data from 2016 to 2020, we calculated disciplinary diversity of 12,225 journals based on paper-level subject classifications. We conducted a systematic analysis comparing Journal Citation Reports (JCR) multidisciplinary journals versus non-multidisciplinary journals. Results showed most JCR multidisciplinary journals have high disciplinary diversity, while non-multidisciplinary journals tend to have lower diversity. Some multidisciplinary journals with low diversity may be misclassified. We also found inconsistencies in journal disciplinary diversity at three granularity levels of the classification system. Visual analysis identified four distribution types of multidisciplinary journal diversity. Furthermore, ten potential multidisciplinary journals were identified in non-multidisciplinary JCR categories. Analysis of these journals showed two distinct publishing behaviors-some continuously publish across multiple fields, while others constantly change focus between fields.
Keywords:
Journal classification system
Multidisciplinary sciences journals
Disciplinary diversity
Science mapping

Journal

Scientometrics cover
Scientometrics
IF:
3.5
Papers:
8.1K
Citations:
2.2W

Organization

C
chinese academy of sciences
Scholars:
56.5W
Papers: 44.9W
Citations: 704