Salīdzināt metodes

	Bibliometriskās kartēšanas pārskats ar bibliometrix atbalstu	Kartēšanas pārskats
Nozare	Zinātnometrija	Zinātnometrija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2017 (bibliometrix tool); mapping review approach formalised c. 2010s	Late 1990s–2000s; major methodological formalization ~2010s
Autors	Aria & Cuccurullo (bibliometrix, 2017); mapping review methodology developed in evidence synthesis community (~2000s)	Buckland & Gann (1998); formalized by systematic review community (Campbell Collaboration, Collaboration for Environmental Evidence)
Tips	Tool-assisted evidence mapping review	Systematic evidence mapping methodology
Pirmavots	Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975.	James, K. L., Randall, N. P., & Haddaway, N. R. (2016). A methodology for systematic mapping in environmental sciences. Environmental Evidence, 5(1), 7.
Citi nosaukumi	bibliometrix mapping review, R-bibliometrix evidence map, bibliometric-assisted systematic map, bibliometrix evidence synthesis map	evidence map, systematic map, research map, literature map
Saistītās	5	6
Kopsavilkums	A bibliometrix-assisted mapping review combines the structured scope-and-search logic of an evidence mapping review with the analytical power of the bibliometrix R package. Instead of manually categorising studies, the researcher leverages bibliometrix functions — keyword co-occurrence networks, thematic clustering, and yearly trend analysis — to chart the landscape of a research field systematically and at scale, producing an interactive, reproducible evidence map.	A mapping review (also called a systematic map or evidence map) is a form of systematic review that aims to chart the extent, range, and nature of evidence on a broad topic rather than synthesize findings into a single pooled answer. It categorizes studies by key dimensions — such as intervention type, population, outcome, and study design — and presents the resulting landscape visually and tabularly so that researchers and practitioners can identify clusters of evidence, knowledge gaps, and priorities for future primary research or deeper synthesis.