Sun Kai, Zhou Liyi, Guo Yike
Data Science Institute, Imperial College London, London SW7 2AZ, UK.
Department of Computing, Imperial College London, London SW7 2AZ, UK.
Innovation (Camb). 2025 May 9;6(6):100945. doi: 10.1016/j.xinn.2025.100945. eCollection 2025 Jun 2.
The peer-review process, which serves as the quality-control mechanism of scientific knowledge production, has been criticized for its bias, unreliability, and inefficiency. Academic conferences and journals typically rely on a centralized mechanism for reviewer assignment and paper assessment. We argue that this centralization is a major factor contributing to the unreliability of the review process, leading to deficiencies in the current knowledge-assessment systems. To address this, we propose a novel decentralized model that democratizes peer review by shifting decision-making rights from centralized authorities to all scholars participating in a scholarly community. Our model includes a dual-rewarding incentive mechanism that motivates scholars to actively participate in peer review by recognizing both their effort and scientific contributions. This model transforms peer review from passive judgment to active collaboration. We simulated the model in conference settings and demonstrated its potential to revolutionize knowledge production and dissemination.
同行评审过程作为科学知识生产的质量控制机制，因其存在偏见、不可靠和效率低下而受到批评。学术会议和期刊通常依赖集中机制进行审稿人分配和论文评估。我们认为这种集中化是导致评审过程不可靠的一个主要因素，进而造成了当前知识评估系统的缺陷。为解决这一问题，我们提出了一种新颖的去中心化模型，该模型通过将决策权从中央权威机构转移到参与学术社区的所有学者手中，实现同行评审的民主化。我们的模型包括一种双重奖励激励机制，通过认可学者的努力和科学贡献来激励他们积极参与同行评审。这种模型将同行评审从被动评判转变为积极协作。我们在会议环境中对该模型进行了模拟，并证明了其有潜力彻底改变知识的生产和传播方式。
