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Abstract

Several characteristics of classical Lorenz curves make them unsuitable for the study of a group of topperformers. TOP-curves, defined as a kind of mirror image of TIP-curves used in poverty studies, are shown to possess the properties necessary for adequate empirical ranking of various data arrays, based on the properties of the highest performers (i.e., the core). TOP-curves and essential TOP-curves, also introduced in this article, simultaneously represent the incidence, intensity, and inequality among the top. It is shown that TOPdominance partial order, introduced in this article, is stronger than Lorenz dominance order. In this way, this article contributes to the study of cores, a central issue in applied informetrics.

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Abstract

One aim of science evaluation studies is to determine quantitatively the contribution of different players (authors, departments, countries) to the whole system. This information is then used to study the evolution of the system, for instance to gauge the results of special national or international programs. Taking articles as our basic data, we want to determine the exact relative contribution of each coauthor or each country. These numbers are brought together to obtain country scores, or department scores, etc. It turns out, as we will show in this article, that different scoring methods can yield totally different rankings. Conseqeuntly, a ranking between countries, universities, research groups or authors, based on one particular accrediting methods does not contain an absolute truth about their relative importance

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Date

14. 2.2012 12:53:22

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Date

29. 2.2008 19:02:46

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Source

Journal of information science. 22(1996) no.3, S.165-170