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Scientific production: A statistical analysis of authors in physics, 1800-1900

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  • John C. Huber

    (Institute for Invention and Innovation)

  • Roland Wagner-Döbler

    (Universität Augsburg)

Abstract

We show that scientific production can be described by two variables: rate of production (rateof publications) and career duration. For 19th century physicists, we show that the time pattern ofproduction is random and Poisson distributed, contrary to the theory of cumulative advantage. Weshow that the exponential distribution provides excellent goodness-of-fit to rate of production andcareer duration. The good fits to these distributions can be explained naturally from the statisticsof exceedances. Thus, more powerful statistical tests and a better theoretical foundation isobtained for rate of production and career duration than has been the case for Lotka's Law.

Suggested Citation

  • John C. Huber & Roland Wagner-Döbler, 2001. "Scientific production: A statistical analysis of authors in physics, 1800-1900," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 437-453, March.
    • Handle: RePEc:spr:scient:v:50:y:2001:i:3:d:10.1023_a:1010558714879
    DOI: 10.1023/A:1010558714879
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    References listed on IDEAS

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    1. John C. Huber & Roland Wagner-Döbler, 2001. "Scientific production: A statistical analysis of authors in mathematical logic," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(2), pages 323-337, February.
    2. R. Wagner-Döbler & J. Berg, 1999. "Physics 1800–1900: A quantitative outline," Scientometrics, Springer;Akadémiai Kiadó, vol. 46(2), pages 213-285, October.
    3. J. C. Huber, 1999. "Inventive productivity and the statistics of exceedances," Scientometrics, Springer;Akadémiai Kiadó, vol. 45(1), pages 33-53, May.
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    Cited by:

    1. Ronald Rousseau, 2002. "Lack of standardisation in informetric research. Comments on “Power laws of research output. Evidence for journals of economics” by Matthias Sutter and Martin G. Kocher," Scientometrics, Springer;Akadémiai Kiadó, vol. 55(2), pages 317-327, August.
    2. Boris Forthmann & Philipp Doebler, 2021. "Reliability of researcher capacity estimates and count data dispersion: a comparison of Poisson, negative binomial, and Conway-Maxwell-Poisson models," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3337-3354, April.

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