October 1978
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5 Reads
Journal for the History of Astronomy
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October 1978
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5 Reads
Journal for the History of Astronomy
July 1978
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2 Reads
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9 Citations
Journal of the American Society for Information Science
September 1976
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186 Reads
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2,132 Citations
Journal of the American Society for Information Science
A Cumulative Advantage Distribution is proposed which models statistically the situation in which success breeds success. It differs from the Negative Binomial Distribution in that lack of success, being a non-event, is not punished by increased chance of failure. It is shown that such a stochastic law is governed by the Beta Function, containing only one free parameter, and this is approximated by a skew or hyperbolic distribution of the type that is widespread in bibliometrics and diverse social science phenomena. In particular, this is shown to be an appropriate underlying probabilistic theory for the Bradford Law, the Lotka Law, the Pareto and Zipf Distributions, and for all the empirical results of citation frequency analysis. As side results one may derive also the obsolescence factor for literature use. The Beta Function is peculiarly elegant for these manifold purposes because it yields both the actual and the cumulative distributions in simple form, and contains a limiting case of an inverse square law to which many empirical distributions conform.
... A fragmentation analysis was performed with an attraction value of 3 and a repulsion value of −3. Additionally, the following basic laws of bibliometric analysis were used [62]: Price's law, through the use of the R 2 coefficient [63]; Lotka's law, to identify the authors who had developed the most studies [64]; and Zipf's law, to determine the terms with the highest occurrence [65]. The academic and research productivity of the authors was determined on the basis of the authors' H-index, which establishes the number of h documents that have been cited a minimum of h times [66], as well as using Lotka's law [67]. ...
September 1976
Journal of the American Society for Information Science