Journal of Informetrics 4(1): table of contents

[Leo Egghe]

Journal of Informetrics

Table of contents of Volume 4, Issue 1, Pages 1-136 (January 2010)


1. Editorial Board
Page CO2,  doi:10.1016/S1751-1577(09)00090-X

2. Citations to scientific articles: Its distribution and dependence on the
article features
Author(s): E.S. Vieira, J.A.N.F. Gomes
Pages 1-13, doi:10.1016/j.joi.2009.06.002
Abstract
The citation counts are increasingly used to assess the impact on the scientific
community of publications produced by a researcher, an institution or a country.
There are many institutions that use bibliometric indicators to steer research
policy and for hiring or promotion decisions. Given the importance that
counting citations has today, the aim of the work presented here is to show how
citations are distributed within a scientific area and determine the dependence
of the citation count on the article features. All articles referenced in the
Web of Science in 2004 for Biology & Biochemistry, Chemistry, Mathematics and
Physics were considered.
We show that the distribution of citations is well represented by a double
exponential-Poisson law. There is a dependence of the mean citation rate on the
number of co-authors, the number of addresses and the number of references,
although this dependence is a little far from the linear behaviour. For the
relation between the mean impact and the number of pages the dependence
obtained was very low. For Biology & Biochemistry and Chemistry we found a
linear behaviour between the mean citation per article and impact factor and
for Mathematics and Physics the results obtained are near to the linear
behaviour.

3. Characteristic scores and scales based on h-type indices
Author(s): L. Egghe
Pages 14-22, doi:10.1016/j.joi.2009.06.001
Abstract
Based on the rank-order citation distribution of e.g. a researcher, one can
define certain points on this distribution, hereby summarizing the citation
performance of this researcher. Previous work of Glänzel and Schubert defined
these so-called “characteristic scores and scales” (CSS), based on average
citation data of samples of this ranked publication–citation list.
In this paper we will define another version of CSS, based on diverse h-type
indices such as the h-index, the g-index, the Kosmulski's h(2)-index and the
g-variant of it, the g(2)-index.
Mathematical properties of these new CSS are proved in a Lotkaian framework.
These CSS also provide an improvement of the single h-type indices in the sense
that they give h-type index values for different parts of the ranked
publication–citation list.

4. q2-Index: Quantitative and qualitative evaluation based on the number and
impact of papers in the Hirsch core
Author(s): F.J. Cabrerizo, S. Alonso, E. Herrera-Viedma, F. Herrera
Pages 23-28,  doi:10.1016/j.joi.2009.06.005
Abstract
Bibliometric studies at the micro level are increasingly requested by science
managers and policy makers to support research decisions. Different measures
and indices have been developed at this level of analysis. One type of indices,
such as the h-index and g-index, describe the most productive core of the output
of a researcher and inform about the number of papers in the core. Other
indices, such as the a-index and m-index, depict the impact of the papers in
the core. In this paper, we present a new index which relates two different
dimensions in a researcher’s productive core: a quantitative one (number of
papers) and a qualitative one (impact of papers). In such a way, we could
obtain a more balanced and global view of the scientific production of
researchers. This new index, called q2-index, is based on the geometric mean of
h-index and the median number of citations received by papers in the h-core,
i.e., the m-index, which allows us to combine the advantages of both kind of
indices.


5. Exposing multi-relational networks to single-relational network analysis
algorithms
Author(s): Marko A. Rodriguez, Joshua Shinavier
Pages 29-41,  doi:10.1016/j.joi.2009.06.004
Abstract
Many, if not most network analysis algorithms have been designed specifically
for single-relational networks; that is, networks in which all edges are of the
same type. For example, edges may either represent “friendship,” “kinship,” or
“collaboration,” but not all of them together. In contrast, a multi-relational
network is a network with a heterogeneous set of edge labels which can
represent relationships of various types in a single data structure. While
multi-relational networks are more expressive in terms of the variety of
relationships they can capture, there is a need for a general framework for
transferring the many single-relational network analysis algorithms to the
multi-relational domain. It is not sufficient to execute a single-relational
network analysis algorithm on a multi-relational network by simply ignoring
edge labels. This article presents an algebra for mapping multi-relational
networks to single-relational networks, thereby exposing them to
single-relational network analysis algorithms.


6. How to modify the g-index for multi-authored manuscripts
Author(s): Michael Schreiber
Pages 42-54,  doi:10.1016/j.joi.2009.06.003
Abstract
A recently suggested modification of the g-index is analysed in order to take
multiple coauthorship appropriately into account. By fractionalised counting of
the papers one can obtain an appropriate measure which I call gm-index. Two
fictitious examples for model cases and two empirical cases are analysed. The
results are compared with two other variants of the g-index which have also
recently been proposed. Only the gm-index shows the correct behaviour when
datasets are aggregated. The interpolated and continuous versions of the
g-index and its variants are also discussed. For an intuitive comparison of the
determination of the investigated variants of the h-index and the g-index, a
visualization of the citation records is utilized.


7. The difference between popularity and prestige in the sciences and in the
social sciences: A bibliometric analysis
Author(s): Massimo Franceschet
Pages 55-63, doi:10.1016/j.joi.2009.08.001
Abstract
The status of a journal is commonly determined by two factors: popularity and
prestige. While the former counts citations, the latter recursively weights
them with the prestige of the citing journals. We make a thorough comparison of
the bibliometric concepts of popularity and prestige for journals in the
sciences and in the social sciences. We find that the two notions diverge more
for the hard sciences, including physics, engineering, material sciences, and
computer sciences, than they do for the geosciences, for biology-medical
disciplines, and for the social sciences. Moreover, we identify the science and
social science journals with the highest diverging ranks in popularity and
prestige compilations.

8. The Hirsch spectrum: A novel tool for analyzing scientific journals
Author(s): Fiorenzo Franceschini, Domenico Maisano
Pages 64-73,  doi:10.1016/j.joi.2009.08.003
Abstract
This paper introduces the Hirsch spectrum (h-spectrum) for analyzing the
academic reputation of a scientific journal. h-Spectrum is a novel tool based
on the Hirsch (h) index. It is easy to construct: considering a specific
journal in a specific interval of time, h-spectrum is defined as the
distribution representing the h-indexes associated to the authors of the
journal articles. This tool allows defining a reference profile of the typical
author of a journal, compare different journals within the same scientific
field, and provide a rough indication of prestige/reputation of a journal in
the scientific community. h-Spectrum can be associated to every journal. Ten
specific journals in the Quality Engineering/Quality Management field are
analyzed so as to preliminarily investigate the h-spectrum characteristics.

9. Can epidemic models describe the diffusion of topics across disciplines?
Author(s): Istvan Z. Kiss, Mark Broom, Paul G. Craze, Ismael Rafols
Pages 74-82,  doi:10.1016/j.joi.2009.08.002
Abstract
This paper introduces a new approach to describe the spread of research topics
across disciplines using epidemic models. The approach is based on applying
individual-based models from mathematical epidemiology to the diffusion of a
research topic over a contact network that represents knowledge flows over the
map of science—as obtained from citations between ISI Subject Categories. Using
research publications on the protein class kinesin as a case study, we report a
better fit between model and empirical data when using the citation-based
contact network. Incubation periods on the order of 4–15.5 years support the
view that, whilst research topics may grow very quickly, they face difficulties
to overcome disciplinary boundaries.

10. Citation speed as a measure to predict the attention an article receives: An
investigation of the validity of editorial decisions at Angewandte Chemie
International Edition
Author(s): Lutz Bornmann, Hans-Dieter Daniel
Pages 83-88,  doi:10.1016/j.joi.2009.09.001
Abstract
The scientific quality of a publication can be determined not only based on the
number of times it is cited but also based on the speed with which its content
is disseminated in the scientific community. In this study we tested whether
manuscripts that were accepted by Angewandte Chemie International Edition (one
of the prime chemistry journals worldwide) received the first citation after
publication faster than manuscripts that were rejected by the journal but
published elsewhere. The results of a Cox regression model show that accepted
manuscripts have a 49% higher hazard rate of citation than rejected
manuscripts.

11. Analysis of cooperative research and development networks on Japanese
patents
Author(s): Hiroyasu Inoue, Wataru Souma, Schumpeter Tamada
Pages 89-96, doi:10.1016/j.joi.2009.09.002
Abstract
To sustain economic growth, countries have to manage systems in order to create
technological innovation. To meet this goal, they are developing policies that
organically connect companies, national laboratories, and universities into
innovation networks. However, the whole structures of these connections have
been little investigated because of the difficulty of obtaining such data.
We use Japanese patent data and create a network of jointly applying
organizations. This network can be considered as one representation of an
innovation network because patents are seeds of innovation and joint
applications are strong evidence of connections between organizations. We
investigated the structure of the network, especially whether or not the degree
distribution follows a power law. After that, we also propose a model that
generates the actual network, not only degree distribution, but also link
distance distribution.

12. The impact of small world on innovation: An empirical study of 16 countries
Author(s): Zifeng Chen, Jiancheng Guan
Pages 97-106,  doi:10.1016/j.joi.2009.09.003
Abstract
This paper investigates the impact of small world properties and the size of
largest component on innovation performance at national level. Our study adds
new evidence to the limited literature on this topic with an empirical
investigation for the patent collaboration networks of 16 main innovative
countries during 1975–2006. We combine small world network theory with
statistical models to systematically explore the relationship between network
structure and patent productivity. Results fail to support that the size of
largest component enhances innovative productivity significantly, which is not
consistent with recent concerns regarding positive effects of largest component
on patent output. We do find that small-world structure benefits innovation but
it is limited to a special range after which the effects inversed and shorter
path length always correlates with increased innovation output. Our findings
extend the current literature and they can be implicated for policy makers and
relevant managers when making decisions for technology, industry and firm
location.

13. Ranking marketing journals using the Google Scholar-based hg-index
Author(s): Salim Moussa, Mourad Touzani
Pages 107-117,  doi:10.1016/j.joi.2009.10.001
Abstract
This paper provides a ranking of 69 marketing journals using a new Hirsch-type
index, the hg-index which is the geometric mean of hg. The applicability of
this index is tested on data retrieved from Google Scholar on marketing journal
articles published between 2003 and 2007. The authors investigate the
relationship between the hg-ranking, ranking implied by Thomson Reuters’
Journal Impact Factor for 2008, and rankings in previous citation-based studies
of marketing journals. They also test two models of consumption of marketing
journals that take into account measures of citing (based on the hg-index),
prestige, and reading preference.

14. Hirsch-type characteristics of the tail of distributions. The generalised
h-index
Author(s): Wolfgang Glänzel, András Schubert
Pages 118-123,  doi:10.1016/j.joi.2009.10.002
Abstract
In this paper a generalisation of the h-index and g-index is given on the basis
of non-negative real-valued functionals defined on subspaces of the vector
space generated by the ordered samples. Several Hirsch-type measures are
defined and their basic properties are analysed. Empirical properties are
illustrated using examples from the micro- and meso-level. Among these
measures, the h-index proved the most, the arithmetic and geometric g-indices,
the least robust measures. The μ-index and the harmonic g-index provide
more balanced results and are still robust enough.

15. Using the Web for research evaluation: The Integrated Online Impact
indicator
Author(s): Kayvan Kousha, Mike Thelwall, Somayeh Rezaie
Pages 124-135,  doi:10.1016/j.joi.2009.10.003
Abstract
Previous research has shown that citation data from different types of Web
sources can potentially be used for research evaluation. Here we introduce a
new combined Integrated Online Impact (IOI) indicator. For a case study, we
selected research articles published in the Journal of the American Society for
Information Science & Technology (JASIST) and Scientometrics in 2003. We
compared the citation counts from Web of Science (WoS) and Scopus with five
online sources of citation data including Google Scholar, Google Books, Google
Blogs, PowerPoint presentations and course reading lists. The mean and median
IOI was nearly twice as high as both WoS and Scopus, confirming that online
citations are sufficiently numerous to be useful for the impact assessment of
research. We also found significant correlations between conventional and
online impact indicators, confirming that both assess something similar in
scholarly communication. Further analysis showed that the overall percentage
for unique Google Scholar citations outside the WoS were 73% and 60% for the
articles published in JASIST and Scientometrics, respectively. An important
conclusion is that in subject areas where wider types of intellectual impact
indicators outside the WoS and Scopus databases are needed for research
evaluation, IOI can be used to help monitor research performance.

Copyright © 2009 Published by Elsevier B.V.