手法を比較

	系統的マッピングレビュー	共引用分析
分野	計量書誌学	計量書誌学
系統	Process / pipeline	Process / pipeline
提唱年	2005	1973
提唱者	Arksey & O'Malley (2005); Joanna Briggs Institute methodology	Henry Small
種類	Method	Method
原典	Arksey, H., & O'Malley, L. (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8(1), 19–32.	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.
別名	scoping review, systematic mapping, literature mapping, evidence mapping	co-citation mapping, historiograph, direct citation, citation pair analysis
関連	4	5
概要	A systematic mapping review (also called a 'scoping review') is a literature review methodology that aims to comprehensively identify and categorize the published evidence on a topic without necessarily assessing the quality of individual studies or synthesizing findings quantitatively. Developed by Arksey and O'Malley (2005) and formalized by the Joanna Briggs Institute, systematic mapping reviews chart the landscape of evidence: what has been studied, what are the research gaps, and how is evidence distributed across study types, populations, and outcomes. Unlike systematic reviews that answer specific research questions with rigorous study selection and synthesis, mapping reviews provide a broad overview of the research terrain, making them ideal for defining scope, identifying knowledge gaps, and guiding future research priorities.	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.