标题： 基于开放同行评审的个体智慧度量的论文质量评估 显示英文标题

标题： Paper Quality Assessment based on Individual Wisdom Metrics from Open Peer Review

摘要： 本研究提出了一种数据驱动的框架，通过开放、自下而上的过程估计审稿人质量，从而提高科学同行评审的准确性和效率。 传统的封闭式同行评审系统虽然对质量控制至关重要，但通常速度慢、成本高，并且容易受到可能阻碍科学进步的偏见影响。 在此，我们介绍了一种方法，通过量化审稿人与社区共识评分的一致性，并应用贝叶斯加权来优化论文质量评估。 我们分析了两个主要科学会议的开放同行评审数据，并证明审稿人特定的质量评分显著提高了论文质量估计的可靠性。 令人意外的是，我们发现审稿人质量评分与作者质量无关。 我们的模型结合了激励机制，以认可高质量的审稿人并鼓励更广泛地覆盖提交的论文，从而缓解社交媒体常见的“富者愈富”的弊端。 这些发现表明，具有估计和激励审稿人质量机制的开放同行评审，为科学出版提供了一个可扩展且公平的替代方案，有望提高同行评审过程的速度、公平性和透明度。 显示英文摘要

摘要： This study proposes a data-driven framework for enhancing the accuracy and efficiency of scientific peer review through an open, bottom-up process that estimates reviewer quality. Traditional closed peer review systems, while essential for quality control, are often slow, costly, and subject to biases that can impede scientific progress. Here, we introduce a method that evaluates individual reviewer reliability by quantifying agreement with community consensus scores and applying Bayesian weighting to refine paper quality assessments. We analyze open peer review data from two major scientific conferences, and demonstrate that reviewer-specific quality scores significantly improve the reliability of paper quality estimation. Perhaps surprisingly, we find that reviewer quality scores are unrelated to authorship quality. Our model incorporates incentive structures to recognize high-quality reviewers and encourage broader coverage of submitted papers, thereby mitigating the common "rich-get-richer" pitfall of social media. These findings suggest that open peer review, with mechanisms for estimating and incentivizing reviewer quality, offers a scalable and equitable alternative for scientific publishing, with potential to enhance the speed, fairness, and transparency of the peer review process.