Σύγκριση μεθόδων

	Αφηγηματική ανασκόπηση με υποστήριξη bibliometrix	Ανάλυση Συν-αναφορών
Πεδίο	Επιστημομετρία	Βιβλιομετρία
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017 (bibliometrix package); narrative review methodology is older	1973
Δημιουργός	Aria & Cuccurullo (bibliometrix R package); narrative review as a traditional form predates this tool	Henry Small
Τύπος	Mixed quantitative-qualitative review methodology	Method
Θεμελιώδης πηγή	Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.	Small, H. (1973). Co-citation in the scientific literature: A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24(4), 265–269.
Εναλλακτικές ονομασίες	bibliometrix narrative review, R-bibliometrix narrative synthesis, quantitative-assisted narrative review	co-citation mapping, historiograph, direct citation, citation pair analysis
Συναφείς	6	5
Σύνοψη	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.	Co-citation analysis is a method that identifies the intellectual structure of a research domain by examining how frequently pairs of documents are cited together in other publications. When two papers are frequently cited together in the literature, they are considered co-cited, indicating they are conceptually related or influential within the same research community. Developed by Henry Small in 1973, co-citation analysis maps the 'invisible colleges' of science—networks of researchers working on related problems—and reveals how knowledge domains evolve over time.