Salīdzināt metodes

	Meta-regresijas balstīta kopīgo vārdu analīze	Zinātnes kartēšana
Nozare	Zinātnometrija	Bibliometrija
Saime	Process / pipeline	Process / pipeline
Izcelsmes gads	2000s–2010s (hybrid application period)	2000s
Autors	Derived from Callon et al. (co-word analysis, 1983) and Glass (meta-regression lineage, 1976); hybrid application developed incrementally in scientometrics and evidence synthesis	Katy Börner, Chaomei Chen, and others
Tips	Hybrid scientometric-statistical method	Method
Pirmavots	Callon, M., Courtial, J. P., Turner, W. A., & Bauin, S. (1983). From translations to problematic networks: An introduction to co-word analysis. Social Science Information, 22(2), 191–235.	Börner, K., Chen, C., & Boyack, K. W. (2003). Visualizing knowledge domains. Annual Review of Information Science and Technology, 37, 179–255.
Citi nosaukumi	MR-CWA, meta-regression co-word mapping, regression-weighted co-word analysis, co-word meta-regression	knowledge mapping, domain mapping, research landscape visualization
Saistītās	4	5
Kopsavilkums	Meta-regression-based co-word analysis is a hybrid scientometric technique that enriches traditional co-word mapping by weighting keyword co-occurrence networks with meta-regression-derived effect estimates. Instead of treating all documents as equally informative, the method uses statistical regression to incorporate study-level moderators — such as publication year, sample size, or methodological quality — into the co-occurrence structure, revealing how thematic clusters in a research field vary across moderator conditions.	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.