Journal impact factors (IFs) can be considered historically as the first attempt to normalize citation distributions by using averages over 2 years. However, it has been recognized that citation distributions vary among fields of science and that one needs to normalize for this. Furthermore, the mean—or any central-tendency statistics—is not a good representation of the citation distribution because these distributions are skewed. Important steps have been taken to solve these two problems during the last few years. First, one can normalize at the article level using the citing audience as the reference set. Second, one can use non-parametric statistics for testing the significance of differences among ratings. A proportion of most-highly cited papers (the top-10% or top-quartile) on the basis of fractional counting of the citations may provide an alternative to the current IF. This indicator is intuitively simple, allows for statistical testing, and accords with the state of the art.
Using aggregated journal–journal citation networks, the measurement of the knowledge base in empirical systems is factor-analyzed in two cases of interdisciplinary developments during the period 1995–2005: (i) the development of nanotechnology in the natural sciences and (ii) the development of communication studies as an interdiscipline between social psychology and political science. The results are compared with a case of stable development: the citation networks of core journals in chemistry. These citation networks are intellectually organized by networks of expectations in the knowledge base at the specialty (that is, above-journal) level. The “structuration” of structural components (over time) can be measured as configurational information. The latter is compared with the Shannon-type information generated in the interactions among structural components: the difference between these two measures provides us with a measure for the redundancy generated by the specification of a model in the knowledge base of the system. This knowledge base incurs (against the entropy law) to variable extents on the knowledge infrastructures provided by the observable networks of relations.
The aggregated journal-journal citation matrix of the Journal Citation Report 2001of the Social Science Citation Indexis analyzed as a single domain in terms of both its eigenvectors and the bi-connected components contained in it. The traditional disciplines (e.g., economics, psychology, or political science) can be retrieved using both methods. These main disciplines do interact marginally. The space between them is occupied by a large number of small clusters of journals indicating specialties that gravitate among the major disciplines. These specialties operate in a mode different from that of the disciplines. For example, the impact factors are low on average and the developments remain volatile. Factor analysis enables us to study how the smaller bi-connected components are related to the larger ones. Factor analysis also highlights methodological differences among groups which may be theoretically connected in a single bi-component.
The journal set which provides a representation of nanoscience and nanotechnology at the interfaces among applied physics,
chemistry, and the life sciences is developing rapidly because of the introduction of new journals. The relevant contributions
of nations can be expected to change according to the representations of the relevant interfaces among journal sets. In the
2005 set the position of the USA decreased more than in the 2004-set, while the EU-27 gained in terms of its percentage of
world share of citations. The tag “Y01N” which was newly added to the EU classification system for patents, allows for the visualization of national
profiles of nanotechnology in terms of relevant patents and patent classes.
The two Journal Citation Reports of the Science Citation Index 2004 and the Social Science Citation Index 2004 were combined in order to analyze and map journals
and specialties at the edges and in the overlap between the two databases. For journals which belong to the overlap (e.g.,
Scientometrics), the merger mainly enriches our insight into the structure which can be obtained from the two databases separately; but
in the case of scientific journals which are more marginal in either database, the combination can provide a new perspective
on the position and function of these journals (e.g., Environment and Planning B — Planning and Design). The combined database additionally enables us to map citation environments in terms of the various specialties comprehensively.
Using the vector-space model, visualizations are provided for specialties that are parts of the overlap (information science,
science & technology studies). On the basis of the resulting visualizations, “betweenness” — a measure from social network
analysis — is suggested as an indicator for measuring the interdisciplinarity of journals.
University-industry-government relations provide a networked infrastructure for knowledge-based innovation systems. This infrastructure organizes the dynamic fluxes locally and the knowledge base remains emergent given these conditions. Whereas the relations between the institutions can be measured as variables, the interacting fluxes generate a probabilistic entropy. The mutual information among the three institutional dimensions provides us with an indicator of this entropy. When this indicator is negative, self-organization can be expected. The self-organizing dynamic may temporarily be stabilized in the overlay of communications among the carrying agencies. The various dynamics of Triple Helix relations at the global and national levels, in different databases, and in different regions of the world, are distinguished by applying this indicator to scientometric and webometric data.
Can change in citation patterns among journals be used as an indicator of structural change in the organization of the sciences? Aggregated journal-journal citations for 1999 are compared with similar data in the Journal Citation Reports1998 of the Science Citation Index. In addition to indicating local change, probabilistic entropy measures enable us to analyze changes in distributions at different levels of aggregation. The results of various statistics are discussed and compared by elaborating the journal-journal mappings. The relevance of this indicator for science and technology policies is further specified
Using percentage performance shares of individual member states, the European Union can be assessed as if it were a network publication system. The prediction of systemness (based on the Markov property of the distribution) can be tested against the predictions of trend lines for individual nations. The publication performance of the EU can also be compared to that of the USA and Japan. The results suggest that a comparison with (global) world trade is important for understanding developments between the various R&D systems. Predictions for the 1999 indicator values are also provided.