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GDP baseline ranking of scientific performance

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  • Gangan Prathap

    (Kerala State Higher Education Council)

Abstract

We use an approach based on GDP baselines to evaluate the relative scientific performance of countries. The measure for scientific impact relies on a database that consolidates the Shanghai ARWU Rankings from 2017 to 2023 and available in the public domain. Two key measures of a size-dependent input and size-independent intensity of output to input are computed and used to present the rankings in the form of Lorenz curves and two-dimensional graphs. The latter reveal a pyramidal or inverted funnel dispersion. Examples are given to illustrate the use of data and methodology and typical results for a few comparator countries as well as all countries that make it to the Academic Ranking of World Universities (ARWU) list are shown. An example is also provided where the population baseline replaces the Gross Domestic Product (GDP) baseline. Finally, a two-dimensional graph based on four parameters from these two alternative baselines gives a composite performance map that allows all comparator countries to be placed within an envelope.

Suggested Citation

  • Gangan Prathap, 2025. "GDP baseline ranking of scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(2), pages 1237-1251, February.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:2:d:10.1007_s11192-025-05250-0
    DOI: 10.1007/s11192-025-05250-0
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    References listed on IDEAS

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    1. Jacek Pietrucha, 2018. "Country-specific determinants of world university rankings," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1129-1139, March.
    2. Gangan Prathap & Ronald Rousseau, 2023. "The modified repeat rate described within a thermodynamic framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 3185-3195, May.
    3. Domingo Docampo & Lawrence Cram, 2015. "On the effects of institutional size in university classifications: the case of the Shanghai ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1325-1346, February.
    4. Caroline S. Wagner & Lin Zhang & Loet Leydesdorff, 2022. "A discussion of measuring the top-1% most-highly cited publications: quality and impact of Chinese papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(4), pages 1825-1839, April.
    5. D. Docampo & D. Egret & L. Cram, 2015. "The effect of university mergers on the Shanghai ranking," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 175-191, July.
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