4.5 Article

Role taxonomy of green and sustainable science and technology journals: exportation, importation, specialization and interdisciplinarity

期刊

SCIENTOMETRICS
卷 126, 期 5, 页码 3871-3892

出版社

SPRINGER
DOI: 10.1007/s11192-021-03939-6

关键词

Journal taxonomy; EBDI; Interdisciplinarity; Specialization; Green and sustainable science and technology

资金

  1. University of Gavle

向作者/读者索取更多资源

Sustainability science is a topic inherently interdisciplinary and focused on solving societal problems. This paper proposed a classification of scientific journals in the Green and Sustainable Science and Technology category for the period 2014-2018 using an entropy-based disciplinarity indicator. The study found that most journals in this field are specialized or interdisciplinary, with interdisciplinary journals showing greater stability over the 5-year period analyzed, and that the number of citations is the most discriminating variable.
Sustainability science is, per se, a topic that is inherently interdisciplinarity and oriented towards the resolution of societal problems. In this paper, we propose a classification of scientific journals that composes the journal category Green and Sustainable Science and Technology in the period 2014-2018 through the entropy-based disciplinarity indicator (EBDI). This indicator allows the classification of scientific journals in four types based on the citing and cited dimensions: knowledge importer, knowledge exporter, disciplinary and interdisciplinarity. Moreover, the relationship between this taxonomy and the JCR bibliometric indicators and its predictive capacity of the taxonomy is explored through a CHAID tree. As well, relations between the Web of Science categories, journals and taxonomy are explored by the co-occurrence of categories and correspondence analysis. Results suggest that the great majority of journals in this field are specialized or interdisciplinary. However, over the 5-year period proposed in this study, interdisciplinary journals tend to be far more stable than specialized ones. The decision tree has shown that the number of citations is the variable with the greatest discriminating capacity.

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