Comparer des méthodes

	Revue narrative assistée par bibliométrie	Cartographie scientifique
Domaine	Scientométrie	Bibliométrie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2017 (bibliometrix package); narrative review methodology is older	2000s
Auteur d'origine	Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool	Katy Börner, Chaomei Chen, and others
Type	Mixed quantitative-qualitative review methodology	Method
Source fondatrice	Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
Alias	bibliometrix narrative review, R-bibliometrix narrative synthesis, quantitative-assisted narrative review	knowledge mapping, domain mapping, research landscape visualization
Apparentées	6	5
Résumé	A bibliometrix-assisted narrative review combines the quantitative field-mapping capabilities of the bibliometrix R package with the interpretive flexibility of a traditional narrative review. Bibliometric indicators — publication trends, author and country productivity, co-citation networks, keyword co-occurrence — are computed and visualised first to orient the reviewer, then a discursive, thematic narrative synthesises the intellectual content of key sources. The result is a structured yet flexible overview of a field that is more transparent and reproducible than a purely informal narrative.	Science mapping is a bibliometric visualization method that creates visual representations of research domains, showing the structure, development, and relationships of scientific fields. Using bibliographic data (citations, keywords, authors, journals), science mapping algorithms generate network diagrams where nodes represent documents, concepts, or authors and edges represent relationships (citation, collaboration, semantic similarity). The resulting maps make invisible intellectual structures visible, enabling researchers to understand field topology, identify emerging areas, and navigate disciplinary landscapes. Pioneered by Börner, Chen, and Boyack in the 2000s, science mapping has become a standard tool in research evaluation and strategic planning.