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  • × theme_ss:"Informetrie"
  1. Nicholls, P.T.: Empirical validation of Lotka's law (1986) 0.20
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    Object
    Lotka-Gesetz
    Source
    Information processing and management. 22(1986), S.417-419
  2. Chongde, W.; Zhe, W.: Evaluation of the models for Bradford's law (1998) 0.10
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    Date
    22. 5.1999 19:12:28
    Object
    Bradford-Gesetz
  3. Rousseau, R.: Informetric laws (2009) 0.09
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    Object
    Bradford-Gesetz
    Lotka-Gesetz
    Zipf-Gesetz
    Mandelbrot-Gesetz
    Leimkuhler-Gesetz
  4. Aiyepekn, W.O.: ¬The Bradford distribution theory (1977) 0.08
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    Bradford-Gesetz
  5. Brookes, B.C.: Theory of the Bradford law (1977) 0.08
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    Bradford-Gesetz
  6. Brookes, B.C.: Bradford' law and the bibliography of science (1969) 0.08
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    Bradford-Gesetz
  7. Murphy, L.J.: Lotka's law in the humanities? (1973) 0.08
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    Lotka-Gesetz
  8. Rousseau, R.: ¬A table for estimating the exponent in Lotka's law (1993) 0.08
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    Lotka-Gesetz
  9. Lotka, A.J.: ¬The frequency distribution of scientific productivity (1926) 0.08
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    Lotka-Gesetz
  10. Pao, M.L.: ¬An empirical examination of Lotka's law (1986) 0.08
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  11. Huang, M.-H.; Huang, W.-T.; Chang, C.-C.; Chen, D. Z.; Lin, C.-P.: The greater scattering phenomenon beyond Bradford's law in patent citation (2014) 0.08
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    Date
    22. 8.2014 17:11:29
    Object
    Bradford-Gesetz
  12. Rousseau, R.: On Egghe's construction of Lorenz curves (2007) 0.07
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    Object
    Zipf-Gesetz
    Lotka-Gesetz
  13. Bookstein, A.: ¬The bibliometric distributions (1976) 0.07
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    Lotka-Gesetz
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  14. Fairthorne, R.A.: Bradford's law and perspective (1980) 0.06
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  15. Egghe, L.: Special features of the author - publication relationship and a new explanation of Lotka's law based on convolution theory (1994) 0.06
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  16. Burrell, Q.L.: Fitting Lotka's law : some cautionary observations on a recent paper by Newby et al. (2003) (2004) 0.06
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  17. Egghe, L.: ¬A new short proof of Naranan's theorem, explaining Lotka's law and Zipf's law (2010) 0.05
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  18. Ufen, F.: ¬Das System als Roman : Eine Regelmäßigkeit der Worthäufigkeit passt bemerkenswerterweise auch auf Unternehmensgrößen und die Größe von Städten (2015) 0.04
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    Abstract
    Im Jahre 2010 hatte New York 8 175 000 Einwohner. Los Angeles war mit 3 793 000 Menschen die zweitgrößte Stadt der Vereinigten Staaten. Dann folgten Chicago mit 2 696 000, Houston mit 2 100 000 und Philadelphia mit 1 526 000 Einwohnern. Zwischen den Einwohnerzahlen sämtlicher amerikanischer Städte besteht ein verblüffender statistischer Zusammenhang: Die zweitgrößte Stadt der USA hat ungefähr halb so viele Einwohner wie New York, die drittgrößte Stadt hat etwa ein Drittel der Einwohnerzahl New Yorks, die viertgrößte Stadt etwa ein Viertel, die fünftgrößte etwa ein Fünftel, usw., usw. Dieser statistische Zusammenhang zwischen dem Rangplatz einer Stadt und ihrer Einwohnerzahl bedeutet, dass die Bevölkerungsverteilung dem Zipfschen Gesetz gehorcht - d.h. die Einwohnerzahl jeder Stadt verhält sich umgekehrt proportional zu ihrer Position in einer absteigenden Rangfolge. Der US-amerikanische Linguist George Kingsley Zipf (1902-1950) formulierte das nach ihm benannte Gesetz allerdings in einem ganz anderen Zusammenhang. Zipf hat entdeckt, dass in Texten jeder beliebigen indoeuropäischen Sprache (zum Beispiel in irgendeinem Roman) das am häufigsten gebrauchte Wort ungefähr doppelt so oft vorkommt wie das am zweithäufigsten verwendete, drei Mal so oft wi das am dritthäufigsten auftauchende usw. Zipf hat außerdem herausgefunden, dass die Wörter einer Sprache, die in der Alltagskommunikation am häufigsten verwendet werden, in aller Regel auch die kürzesten und die ältesten sind. Seine Untersuchungen machten ihn zu einem Pionier der quantitativen Erforschung sprachlicher Phänomene.
    Object
    Zipf-Gesetz
  19. Egghe, L.: Zipfian and Lotkaian continuous concentration theory (2005) 0.04
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  20. Egghe, L.; Guns, R.: Applications of the generalized law of Benford to informetric data (2012) 0.04
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    Object
    Zipf-Gesetz
    Benford-Gesetz

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