Bollen Johan, Crandall David, Junk Damion, Ding Ying, Börner Katy
School of Informatics and Computing, Indiana University, Bloomington, IN, USA.
Indiana University Network Institute, Indiana University, Bloomington, IN, USA.
Scientometrics. 2017 Jan;110(1):521-528. doi: 10.1007/s11192-016-2110-3. Epub 2016 Sep 3.
This paper presents a novel model of science funding that exploits the wisdom of the scientific crowd. Each researcher receives an equal, unconditional part of all available science funding on a yearly basis, but is required to individually donate to other scientists a given fraction of all they receive. Science funding thus moves from one scientist to the next in such a way that scientists who receive many donations must also redistribute the most. As the funding circulates through the scientific community it is mathematically expected to converge on a funding distribution favored by the entire scientific community. This is achieved without any proposal submissions or reviews. The model furthermore funds scientists instead of projects, reducing much of the overhead and bias of the present grant peer review system. Model validation using large-scale citation data and funding records over the past 20 years show that the proposed model could yield funding distributions that are similar to those of the NSF and NIH, and the model could potentially be more fair and more equitable. We discuss possible extensions of this approach as well as science policy implications.
本文提出了一种利用科学界智慧的新型科学资助模式。每位研究人员每年都会平等、无条件地获得所有可用科学资助的一部分,但需要将自己所获资助的一定比例单独捐赠给其他科学家。这样,科学资助就以一种方式从一位科学家转移到另一位科学家手中,即收到许多捐赠的科学家也必须进行最多的再分配。随着资助在科学界循环,从数学角度预计它会趋向于整个科学界所青睐的资助分配。这一过程无需任何提案提交或评审即可实现。此外,该模式资助的是科学家而非项目,减少了当前资助同行评审系统的大量管理费用和偏差。使用过去20年的大规模引用数据和资助记录进行的模型验证表明,所提出的模型能够产生与美国国家科学基金会(NSF)和美国国立卫生研究院(NIH)类似的资助分配,并且该模型可能会更加公平、公正。我们讨论了这种方法可能的扩展以及对科学政策的影响。