Bibliometrics - School of Communication and Information
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Transcript Bibliometrics - School of Communication and Information
BIBLIOMETRICS
Tefko Saracevic
Rutgers University
http://www.scils.rutgers.edu/~tefko
© Tefko Saracevic
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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
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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
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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
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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
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Literature studies
Qualitative
often in humanities, librarianship
Quantitative
bibliometrics
Mixed
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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
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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
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Application in ...
History of science
Sociology of science
Science policy; resource allocation
Library selection, weeding, policies
Information organization
Information management
utilization
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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)
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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 …
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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
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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
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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
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Variable: origin
Rates of production, size, growth by
country, institution, language, subject
Comparison between these
Correlation with economic & other
indicators
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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
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Variable: contents
Analysis of texts
distribution of words – Zipf’s law
words, phrases in various parts
subject analysis, classification
co-word analysis
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Variable: representation
frequency of use of index terms, classes
distribution laws - key terms where?
thesaurus structure
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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
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citations … more
co-citations
author connections, subject structure, networks,
maps
centrality
of authors, papers
validation with qualitative methods
impact
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Variable: utilization
frequency
distribution of requests for sources, titles
e.g. 20/80 law
relevance judgement distributions
circulation patterns
use patterns
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Variable: links
Development of link-based metrics
in-links, out-links
Web structure
Web page depth; update
PageRank vs quality
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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
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Examples of laws & methods
Lotka’s law
Bradford’s law
Zipf’s law
Impact factor
Citation structures
Co-citation structures
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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
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Lotka’s law: xn • y = C
The total number of authors y in a given subject,
each producing x publications, is inversely
proportional to some exponential function n of x.
Where:
x
y
=
=
n
=
C
=
number of publications
no. of authors credited with x
publications
constant (equals 2 for scientific
subjects)
constant
inverse square law of scientific productivity
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No. of authors
Lotka's Law - scientific publications
1 publ.
© Tefko Saracevic
2 publ.
3 publ.
xn • y = C
4 publ.
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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)
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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 …"
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Bradford's Law of Scattering –
an idealized example
No. of
No. of articles
per source
source journals
60
1
3 2
35
30
1
25
2
9 2
9
8
4
6
10
5
7
27 5
4
3
5
© Tefko Saracevic
Total no. of
articles
60
130
70
30
50
18 130
32
60
35
130
20
15
30
Bradford's Law of Scattering – zones
nucleus
3 sources
130 articles
9 sources
130 articles
27 sources
130 articles
© Tefko Saracevic
Garfield hypothesis
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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
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Zipf’s law: r • f = c
Where:
r =
f =
rank (in terms of frequency)
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
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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
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Curve of obsolescence
Age at time of use
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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
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Citation matrix
citing
article
cited
article
cited
article
cited
article
© Tefko Saracevic
article
citing
article
citing
article
citing
article
citing
article
citing
article
citing
article
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Science Citation Index
Association-of-ideas index
citing
article
cited
article
cited
article
cited
article
© Tefko Saracevic
article
citing
article
citing
article
citing
article
citing
article
citing
article
citing
article
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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
citing
article
© Tefko Saracevic
These two
articles are
likely to be
related
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Impact factor (IF)
number of citations received in current year by
papers published in the journal in the previous two
years
divided 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-)
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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
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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
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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
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