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Abstract

The Impact Factors (IFs) of the Institute for Scientific Information suffer from a number of drawbacks, among them the statistics-Why should one use the mean and not the median?-and the incomparability among fields of science because of systematic differences in citation behavior among fields. Can these drawbacks be counteracted by fractionally counting citation weights instead of using whole numbers in the numerators? (a) Fractional citation counts are normalized in terms of the citing sources and thus would take into account differences in citation behavior among fields of science. (b) Differences in the resulting distributions can be tested statistically for their significance at different levels of aggregation. (c) Fractional counting can be generalized to any document set including journals or groups of journals, and thus the significance of differences among both small and large sets can be tested. A list of fractionally counted IFs for 2008 is available online at http:www.leydesdorff.net/weighted_if/weighted_if.xls The between-group variance among the 13 fields of science identified in the U.S. Science and Engineering Indicators is no longer statistically significant after this normalization. Although citation behavior differs largely between disciplines, the reflection of these differences in fractionally counted citation distributions can not be used as a reliable instrument for the classification.

Date

22. 1.2011 12:51:07

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Abstract

Group-based trajectory modeling (GBTM) is applied to the citation curves of articles in six journals and to all citable items in a single field of science (virology, 24 journals) to distinguish among the developmental trajectories in subpopulations. Can citation patterns of highly-cited papers be distinguished in an early phase as "fast-breaking" papers? Can "late bloomers" or "sleeping beauties" be identified? Most interesting, we find differences between "sticky knowledge claims" that continue to be cited more than 10 years after publication and "transient knowledge claims" that show a decay pattern after reaching a peak within a few years. Only papers following the trajectory of a "sticky knowledge claim" can be expected to have a sustained impact. These findings raise questions about indicators of "excellence" that use aggregated citation rates after 2 or 3 years (e.g., impact factors). Because aggregated citation curves can also be composites of the two patterns, fifth-order polynomials (with four bending points) are needed to capture citation curves precisely. For the journals under study, the most frequently cited groups were furthermore much smaller than 10%. Although GBTM has proved a useful method for investigating differences among citation trajectories, the methodology does not allow us to define a percentage of highly cited papers inductively across different fields and journals. Using multinomial logistic regression, we conclude that predictor variables such as journal names, number of authors, etc., do not affect the stickiness of knowledge claims in terms of citations but only the levels of aggregated citations (which are field-specific).

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Abstract

Two methods for comparing impact factors and citation rates across fields of science are tested against each other using citations to the 3,705 journals in the Science Citation Index 2010 (CD-Rom version of SCI) and the 13 field categories used for the Science and Engineering Indicators of the U.S. National Science Board. We compare (a) normalization by counting citations in proportion to the length of the reference list (1/N of references) with (b) rescaling by dividing citation scores by the arithmetic mean of the citation rate of the cluster. Rescaling is analytical and therefore independent of the quality of the attribution to the sets, whereas fractional counting provides an empirical strategy for normalization among sets (by evaluating the between-group variance). By the fairness test of Radicchi and Castellano (), rescaling outperforms fractional counting of citations for reasons that we consider.

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Abstract

The BRICS countries (Brazil, Russia, India, China, and South Africa) are notable for their increasing participation in science and technology. The governments of these countries have been boosting their investments in research and development to become part of the group of nations doing research at a world-class level. This study investigates the development of the BRICS countries in the domain of top-cited papers (top 10% and 1% most frequently cited papers) between 1990 and 2010. To assess the extent to which these countries have become important players at the top level, we compare the BRICS countries with the top-performing countries worldwide. As the analyses of the (annual) growth rates show, with the exception of Russia, the BRICS countries have increased their output in terms of most frequently cited papers at a higher rate than the top-cited countries worldwide. By way of additional analysis, we generate coauthorship networks among authors of highly cited papers for 4 time points to view changes in BRICS participation (1995, 2000, 2005, and 2010). Here, the results show that all BRICS countries succeeded in becoming part of this network, whereby the Chinese collaboration activities focus on the US.

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Date

6.11.2005 19:02:22

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Date

22. 3.2009 19:07:20

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Date

8. 1.2019 18:22:45