Compara mètodes

	Anàlisi cienciomètrica per franges de temps	Anàlisi de Co-cites
Camp	Cienciometria	Bibliometria
Família	Process / pipeline	Process / pipeline
Any d'origen	1980s–1990s	1973
Autor original	Derived from scientometrics tradition; temporal slicing formalized in longitudinal bibliometric studies from the 1980s onward	Henry Small
Tipus	Quantitative longitudinal analysis	Method
Font seminal	Small, H. (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50(9), 799-813.	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.
Àlies	temporal scientometrics, period-based scientometric analysis, time-window scientometrics, longitudinal scientometric analysis	co-citation mapping, historiograph, direct citation, citation pair analysis
Relacionats	6	5
Resum	Time-sliced scientometric analysis divides a bibliographic corpus into discrete temporal windows — commonly five- or ten-year periods — and applies standard scientometric indicators (publication counts, citation rates, h-index, collaboration networks, keyword co-occurrence) within each slice. By comparing results across slices, researchers can reconstruct how a scientific field has grown, shifted focus, formed new collaborations, or declined in influence over time. The approach combines the rigor of quantitative scientometrics with an explicit longitudinal dimension.	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.