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Peer review and over-competitive research funding fostering mainstream opinion to monopoly

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  • Hui Fang

    (Nanjing University)

Abstract

The aim of peer review is to separate the wheat from the chaff for publication and research funding. In the excessive competition, this mechanism would only select the wheat of mainstream. Up to now, almost all discussions on the consequence of the short-comings of peer review are limited to qualitatively description. I propose a model of “peer-group-assessed-grant-based-funding-system” combined with tenure system and over-competitive research funding review process. It is the first on the quantitatively investigation which dramatizes the current short-comings of the process. My simulation shows that it takes about two or three generations of researchers for the mainstream of a complicated research topic obtaining monopoly supremacy, with only the aid of the mechanism the model described. Based on the computation results, suggestions are proposed to avoid loss of self-correction capability on popularity determined single research direction which could be wrong on very complicated research topics.

Suggested Citation

  • Hui Fang, 2011. "Peer review and over-competitive research funding fostering mainstream opinion to monopoly," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(2), pages 293-301, May.
    • Handle: RePEc:spr:scient:v:87:y:2011:i:2:d:10.1007_s11192-010-0323-4
    DOI: 10.1007/s11192-010-0323-4
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    References listed on IDEAS

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    1. Vasiliki Plerou & Luís A. Nunes Amaral & Parameswaran Gopikrishnan & Martin Meyer & H. Eugene Stanley, 1999. "Similarities between the growth dynamics of university research and of competitive economic activities," Nature, Nature, vol. 400(6743), pages 433-437, July.
    2. Peter A. Lawrence, 2003. "The politics of publication," Nature, Nature, vol. 422(6929), pages 259-261, March.
    3. Cameron Neylon, 2009. "Funding ban could break careers at the toss of a coin," Nature, Nature, vol. 459(7247), pages 641-641, June.
    4. Trisha Gura, 2002. "Peer review, unmasked," Nature, Nature, vol. 416(6878), pages 258-260, March.
    5. L. Erik Clavería & Eliseo Guallar & Jordi Camí & José Conde & Roberto Pastor & José R. Ricoy & Eduardo Rodríguez-Farré & Fernando Ruiz-Palomo & Emilio Muñoz, 2000. "Does Peer Review Predict the Performance of Research Projects in Health Sciences?," Scientometrics, Springer;Akadémiai Kiadó, vol. 47(1), pages 11-23, January.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Thomas Feliciani & Junwen Luo & Lai Ma & Pablo Lucas & Flaminio Squazzoni & Ana Marušić & Kalpana Shankar, 2019. "A scoping review of simulation models of peer review," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 555-594, October.
    2. Katarína Cechlárová & Tamás Fleiner & Eva Potpinková, 2014. "Assigning evaluators to research grant applications: the case of Slovak Research and Development Agency," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(2), pages 495-506, May.
    3. Xuan Zhen Liu & Hui Fang, 2012. "Peer review and over-competitive research funding fostering mainstream opinion to monopoly. Part II," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 607-616, February.
    4. Balázs Győrffy & Andrea Magda Nagy & Péter Herman & Ádám Török, 2018. "Factors influencing the scientific performance of Momentum grant holders: an evaluation of the first 117 research groups," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 409-426, October.

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    More about this item

    Keywords

    Peer review; Research funding; Excessive competition; Mainstream; Mathematical model; Simulation;
    All these keywords.

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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