Methoden vergelijken

	Bibliometrische Wetten: Lotka's, Bradford's en Zipf's Wet	Co-citatieanalyse
Vakgebied	Bibliometrie	Bibliometrie
Familie	Process / pipeline	Process / pipeline
Jaar van ontstaan	1926–1949	1973
Grondlegger	Alfred J. Lotka, Samuel C. Bradford, George K. Zipf	Henry Small
Type	Concept	Method
Oorspronkelijke bron	Lotka, A. J. (1926). The frequency distribution of scientific productivity. Journal of the Washington Academy of Sciences, 16(12), 317–323.	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.
Aliassen	bibliometric distributions, productivity laws, frequency laws, information science laws	co-citation mapping, historiograph, direct citation, citation pair analysis
Verwant	3	5
Samenvatting	Three foundational empirical laws describe the structure and distribution of scientific information: Lotka's Law characterizes author productivity (most authors publish few papers; a few publish many), Bradford's Law describes journal concentration (a small number of core journals contain the majority of papers on a topic), and Zipf's Law models word and term frequency (word frequency inversely proportional to its rank). These regularities, discovered in the mid-20th century, are remarkably robust across disciplines and have become essential tools for understanding research productivity, organizing information resources, and designing search strategies.	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.