The study of the statistical regularities observed in the field of information production and use has confirmed the existence of important similarities. Thus, the existence of regularities and measurable ratios allow the prevision and the concept of laws. In the 1950s, Shannon and Weaver [13] modelled the information circulation theory. The entropy hypothesis of this theory is that the more ranked a system is, the less information it produces. Theoretical studies have tried to formalise the connection between the bibliometric distribution and the entropy. In this paper, we try to extend previous results linked with ‘the least effort principle’ and the analytical slope of a bibliometric distribution. In the first and second parts, we recall some statements about entropy and bibliometric distributions and, after that, we describe different links between them.
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References
1.
[1] L. Egghe , On the classification of the classical bibliometric laws , Journal of Documentation44(1) (1988) 53-62 .
2.
[2] L. Egghe , Special features of the author publication relationship and a new explanation of Lotka’s law based on convolution theory , Journal of the American Society for Information Science45(6) (1994) 422-427 .
3.
[3] G. Calot, Cours de Calcul de Probabilité (Dunod Decision, 1984). See Chapter 12.
4.
[4] Y. Caumel, Probabilités, Théorie et Applications (Eyrolles, 1988). See Chapter 8.
5.
Part III: 181-194 .
6.
[6] P.B. Kantor and J.L. Jung , Testing the maximum entropy principle for information retrieval , Journal of the American Society for Information Science49(6) (1998) 557-566 .
7.
[7] T. Lafouge and S. Laine Cruzel , A new explanation of the geometrical law in the case of library circulation data , Information Processing and Management33(4) (1997) 523-527 .
8.
[8] T. Lafouge and E. Guinet , A new explanation of the negative binomial law and the Poisson law with regard to library circulation data , Journal of Information Science25(1) (1999) 89-93 .
9.
[9] J. Lhen, T. Lafouge, Y. Elskens, L. Quoniam and H. Dou, La statistique des lois de Zipf, Revue Française de Bibliométrie 14 (1995) 135-146.
10.
[10] A. Lotka , The frequency distribution of scientific productivity , Journal of the Washington Academy of Sciences16 (1926) 317-323 .
11.
[11] L. Quoniam, Bibliométrie sur les références bibliographiques: méthodologie. In: La VeilleTechnologique: l’Information Scientifique, Technique et Industrielle (Dunod, 1992), pp. 244 and 261.
12.
[12] A. Reyni , On measures of entropy and information. In: J. Newman (ed.), Berkeley Symposium on Mathematical Statistics and Probability (1961).
13.
[13] C. Shannon and W. Weaver, Théorie Mathématique de la Communication (Bibliothèque du CEPL, 1975).
14.
[14] A.L. Yablonsky , On fundamental regularities of the distribution of scientific productivity , Scientometrics2(1) (1980) 3-34 .
