BIBLIOMETRICS

1. BIBLIOMETRICS Tefko Saracevic Rutgers University http://www.scils.rutgers.edu/~tefko © Tefko Saracevic

2. What is? • “… all studies which seek to quantify processes of written communication.” Pritchard • “… the quantitative treatment of the propertiesd of recorded discourse and behavior pertaining to it.” Fairthorne • Recorded communication - ‘literature’-> quantitative methods © Tefko Saracevic

3. Alan Pritchard 1969 • Coined the term "bibliometrics" "the application of mathematics and statistical methods to books and other media of communication“ Journal of Documentation (1969) 25(4):348-349 © Tefko Saracevic

4. and other related metrics … • Also used to study broader than books, articles … • Scientometrics • covering science in general, not just publications • Infometrics • all information objects • Webmetrics or cybermetrics • web connections, manifestations • using bibliometric techniques to study the relationship or properties of different sites on the web © Tefko Saracevic

5. Concepts Basic (primitive) concepts: 1. Subject 2. Recorded communication -> document, information object 3. Subject literature • Bibliometrics related to: • science of science • sociology of science - numerical methods © Tefko Saracevic

6. Literature studies • Qualitative • often in humanities, librarianship • Quantitative • bibliometrics • Mixed © Tefko Saracevic

7. Reasons for quantitative studies of literature • Analysis of structure and dynamics • search for regularities - predictions possible • Understanding of patterns • “order out of documentary chaos” • verification of models, assumptions • Rationale for policies & design © Tefko Saracevic

8. Why quantitative studies? • Qualitative methods often depend on assertions. ‘authoritative’ statements, anecdotal evidence • Science searches for regularities • Success of statistical methods in social sciences • Need for justification & basis for decisions • Something can be counted - irresistible © Tefko Saracevic

9. Application in ... • History of science • Sociology of science • Science policy; resource allocation • Library selection, weeding, policies • Information organization • Information management • utilization © Tefko Saracevic

10. Historical note • Bibliometrics long precedes information science • But found intellectual home in information science • study of a basic phenomenon - literature • It is not ‘hot’ lately, but still produces very interesting results • Branched out into web studies (web is a “literature” as well) © Tefko Saracevic

11. What studied? • Governed by data available in documents or information resources in general - that what can be counted • author(s) • origin • organization, country, language • source • journal, publisher, patent … © Tefko Saracevic

12. what … more • contents • text, parts of text, subject, classes • representation • citations • to a document, in a document, co-citation • utilization • circulation, various uses • links • any other quantifiable attribute © Tefko Saracevic

13. Tools • Science Citation Index • Compilation of variables from journals in a subject • Use data • Publication counts from indexes, or other data bases • Web structures, links © Tefko Saracevic

14. Variable: authors • number in a subject, field, institution, country • growth • correlation with indicators like GNP, energy etc. • productivity e.g. Lotka’s law • collaboration - co-authorship, associated networks • dynamics - productive life, transcience, epidemics • papers/author in a subject • mapping © Tefko Saracevic

15. Variable: origin • Rates of production, size, growth by • country, institution, language, subject • Comparison between these • Correlation with economic & other indicators © Tefko Saracevic

16. Variable: sources • Concentration most often on journals • Growth, dynamics, numbers • information explosion - exponential laws • time movements, life cycles • Scatter - quantity/yield distribution • Bradford’s law • Various distributions • by subject, language, country © Tefko Saracevic

17. Variable: contents • Analysis of texts • distribution of words – Zipf’s law • words, phrases in various parts • subject analysis, classification • co-word analysis © Tefko Saracevic

18. Variable: representation • frequency of use of index terms, classes • distribution laws - key terms where? • thesaurus structure © Tefko Saracevic

19. Variable: citations • Studied a lot; many pragmatic results • base for citation indexes, web of science, impact factors, co-citation studies etc • Derived: • number of references in articles • number of citations to articles • research front; citation classics • bibliographic coup[ling © Tefko Saracevic

20. citations … more • co-citations • author connections, subject structure, networks, maps • centrality • of authors, papers • validation with qualitative methods • impact © Tefko Saracevic

21. Variable: utilization • frequency • distribution of requests for sources, titles • e.g. 20/80 law • relevance judgement distributions • circulation patterns • use patterns © Tefko Saracevic

22. Variable: links • Development of link-based metrics • in-links, out-links • Web structure • Web page depth; update • PageRank vs quality © Tefko Saracevic

23. Examples from classic studies • Comparative publications over centuries • Number of journals founded over time • Number of abstracts published over time • National share of abstracts in chemistry • National scientific size vs. economy size • Bibliographic coupling and co-citation • Web structures, links © Tefko Saracevic

24. Examples of laws & methods • Lotka’s law • Bradford’s law • Zipf’s law • Impact factor • Citation structures • Co-citation structures © Tefko Saracevic

25. Alfred J. Lotka 1926 • Statistics—the frequency distribution of scientific productivity Purpose: to "determine, if possible, the part which men of different calibre contribute to the progress of science“ • Looked at Chemical Abstracts Index, then Geschichtstafeln der Physik • J. Washington Acad. Sci. 16:317-325 © Tefko Saracevic

26. Lotka’s law: xn • y = C The total number of authors yin a given subject, each producing xpublications, is inversely proportional to some exponential functionn of x. • Where: • x = number of publications • y = no. of authors credited with x publications • n = constant (equals 2 for scientific subjects) • C = constant • inverse square law of scientific productivity © Tefko Saracevic

27. Lotka's Law - scientific publications No. of authors xn• y = C © Tefko Saracevic

28. Samuel Clement Bradford 1934, 1948 • Distribution of quantity vs yield of sources of information on specific subjects • he studied journals as sources, but applicable to other • what journals produce how many articles in a subject and how are they distributed? or • How are articles in a subject scattered across journals? • Purpose: to develop a method for identification of the most productive journals in a subject & deal with what he called “documentary chaos” First published in: Engineering (1934) 137:85-86, then in his book Documentation, (1948) © Tefko Saracevic

29. Bradford’s law "If scientific journals are arranged in order of decreasing productivity of articles on a given subject, they may be divided into a nucleus of periodicals more particularly devoted to the subject and several groups or zones containing the same number of articles as the nucleus, when the numbers of periodicals in the nucleus and succeeding zones will be as a : n : n2 : n3 …" © Tefko Saracevic

30. Bradford's Law of Scattering – an idealized example No. of articles per source 60 35 30 25 9 8 6 5 4 3 Total no. of articles 60 70 30 50 18 32 60 35 20 15 No. of source journals 1 2 1 2 2 4 10 7 5 5 3 130 9 130 27 130 © Tefko Saracevic

31. Bradford's Law of Scattering – zones nucleus 3 sources 130 articles 9 sources 130 articles 27 sources 130 articles Garfield hypothesis © Tefko Saracevic

32. George Kingsley Zipf 1935, 1949 • The psycho-biology of language: an introduction to dynamic philology (1935) • Human behavior and the principle of least effort: An introduction to human ecology(1949) • Looked, among others, at frequency distributions of words in given texts • counted distribution in James Joyces’ Ulysses • Provided an explanation as to why the found distributions happen: Principle of least effort © Tefko Saracevic

33. Zipf’s law: r • f = c • Where: r = rank (in terms of frequency) f = frequency (no. of times the given word is used in the text) c = constant for the given text • For a given text the rank of a word multiplied by the frequency is a constant • Works well for high frequency words, not so well for low – thus a number of modifications © Tefko Saracevic

34. Charles F. Gosnell 1944 Obsolescence • He studied obsolescence of books in academic libraries via their use • College Res. Libr. (1994) 5:115-125 • But this was extended to study of articles via citations, and other sources • Age of citations in articles in a subject: • half life – half of the citations are x year old etc • different subjects have very different half-lives © Tefko Saracevic

35. Curve of obsolescence Number of users Age at time of use © Tefko Saracevic

36. Eugene Garfield 1955 • Focused on scientific & scholarly communication based on citations • Science (1995) 122:108-111 • Founded Institute for Scientific Information (ISI) • major proeduct now ISI Web of Knowledge • Impact factor for journals, based on how much is a journal cited • Mapping of a literature in a subject • Citation indexes/web of knowledge • MAJOR resources in bibliometric studies © Tefko Saracevic

37. Citation matrix citing article citing article cited article citing article article citing article cited article citing article citing article cited article citing article © Tefko Saracevic

38. Science Citation Index Association-of-ideas index citing article citing article cited article citing article article citing article cited article citing article citing article cited article citing article © Tefko Saracevic

39. Co-citation analysis Articles that cite the same article are likely to both be of interest to the reader of the cited article citing article article These two articles are likely to be related citing article © Tefko Saracevic

40. Impact factor (IF) number of citations received in current year by papers published in the journal in the previous two yearsdivided by number of papers published in the journal in the previous two years • IF has become over time a crucial indicator of journal quality and • given ISI a monopoly position in the evaluation of journal quality • Reported in Journal Citation Reports (1976-) © Tefko Saracevic

41. Garfield’s HistCite • “Bibiliographic Analysis and Visualization Software” • Provides citation statistics & graphs for people, journals, institutions … • various citations scores, no. of cited references in articles … various graphs with connections • Example: articles and authors for JASIST (and predecessor names) for 1956-2004 • includes citations to authors © Tefko Saracevic

42. Conclusion • Bibliometrics, & related scientometrics, infometrics, webmetrics provide insight into a number of properties of information objects • some general, predictive “laws” formulated • structures have been exposed, graphed • myriad data collected & analyzed • A good area for research! © Tefko Saracevic

43. Sources used in making this presentation– among others • Ruth Palmquist Bibliometrics • Donna Bair-Mundy Boolean, bibliometrics, and beyond • Short set of bibliometric exercises by J. Downie http://people.lis.uiuc.edu/~jdownie/biblio/ © Tefko Saracevic