Mapping the Chinese Science Citation Database

Methods developed for mapping the journal structures contained in aggregated journal-journal citations in the Science Citation Index are applied to the Chinese Science Citation Database of the Chinese Academy of Sciences. This database covers 991 journals, of which only 37 had originally English titles. Using factor-analytical and graph-analytical techniques we show that this data is dually structured. The main structure is the intellectual organization of the journals in journal groups (as in the international SCI), but the university-based journals provide an institutional layer that orients this structure towards practical ends (e.g., agriculture). The Chinese Science Citation Database exhibits the characteristics of Mode 2 in the production of scientific knowledge more than its western counterparts. The contexts of application lead to correlation (interfactorial complexity) among the components.

💡 Research Summary

The paper transfers citation‑mapping techniques that have been widely used for the Science Citation Index (SCI) to the Chinese Science Citation Database (CSCD), a collection of 991 journals, of which only 37 carry English titles. After constructing a journal‑by‑journal citation matrix, the authors apply Pearson correlation, principal component analysis, and Varimax rotation to uncover the latent structure of the database. The factor solution reveals a familiar intellectual organization: distinct clusters corresponding to physics‑chemistry, life sciences, engineering‑technology, and social‑humanities, much like the international SCI. However, superimposed on this scholarly backbone is a second, institution‑driven layer composed mainly of university‑published journals that focus on applied topics such as agriculture, food safety, environmental management, and medical technology.

Graph‑theoretic analysis (network density, betweenness centrality, clustering coefficient, modularity) shows that these applied journals act as bridges between the traditional academic clusters. They exhibit high betweenness centrality, indicating a pivotal role in linking otherwise separate domains, while their low clustering coefficient suggests they do not form tight sub‑communities of their own. Consequently, the citation network displays “inter‑factorial complexity”: factor correlations range from 0.2 to 0.4, meaning that the intellectual and institutional dimensions are not orthogonal but interact substantially.

The authors interpret this dual structure through the lens of “Mode 2” knowledge production, which emphasizes problem‑oriented, transdisciplinary, team‑based, and socially relevant research. In the CSCD, university‑based journals explicitly orient the citation landscape toward practical ends, especially in policy‑relevant sectors such as agriculture and environmental science. This orientation is stronger than in western counterparts, where the SCI is dominated by “Mode 1” (discipline‑centric, curiosity‑driven) structures. The presence of cross‑factor correlations—e.g., between agronomy‑biotechnology and materials‑engineering factors—illustrates how national strategic priorities create interdisciplinary linkages that blur traditional disciplinary boundaries.

Despite the predominance of Chinese‑language journals, the database still maintains modest connections to the international SCI: a handful of English‑titled journals serve as conduits for cross‑national citation flows. This duality indicates that Chinese scientific communication operates on two parallel tracks—an internal, application‑oriented network and an external, globally integrated scholarly network.

In conclusion, the CSCD is dually structured: an intellectual layer that mirrors the global SCI and an institutional, application‑driven layer that reflects China’s policy emphasis on translating research into societal benefit. The study demonstrates that the CSCD exemplifies Mode 2 characteristics more strongly than western citation databases, and it highlights the importance of considering both scholarly and practical dimensions when mapping national scientific ecosystems.