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授予审查评估的新方法:得分,然后排名
Stephen A Gallo1, Michael Pearce2, Carole J Lee3
1American Institute of Biological Sciences, Washington D.C., United States. sgalloster@gmail.com.
Research integrity and peer review
|July 24, 2023
概括
本研究引入了一种新方法,将提案评级和排名结合起来,以便更好地做出资助决策. 这种综合方法提高了准确性,并解决了传统审查过程中的模两可.
科学领域:
- 科学同行评审和赠款资金分配.
- 用于偏好聚合的统计建模.
背景情况:
- 赠款提案的资金通常基于审查评级的总结统计数据,这可能会导致诸如联系和不清楚的资金优先级等问题.
- 现有的方法不能同时分析数值评级和顺序排名,这限制了融资决策的准确性.
- 将排名数据 (例如,top-k偏好) 与评级相结合,可以强制执行更清晰的排序,并减轻仅评级方法的局限性.
研究的目的:
- 提出一个实用的方法来整合提案评级和排名在同行评审评估.
- 展示一个联合模型 (Mallows-双项) 的实用性,用于生成一个综合的分数和偏好排序.
- 展示这种方法在现实世界的赠款审查设置中的应用,以改进资助决策.
主要方法:
- 应用了马洛斯-双项联合模型来分析综合评级和排名数据.
- 开发了一种创新的协议,用于收集顶级k排名,作为标准评分程序的辅助.
- 利用理论示例和一个用实际同行评审数据的案例研究来验证方法.
主要成果:
- 综合模型成功地解决了联系,并为提案提供了明确的优先顺序,即使是部分排名或不一致的审查员评估.
- 展示了将排名纳入如何改善仅以评级为基础的方法,特别是对于具有异常分数的提案.
- 现实世界小组数据分析证实了该方法能够提供准确的资金优先级信息,适用于决策.
结论:
- 在同行评审中收集和分析评级和排名数据的强有力的方法已经建立.
- 这种综合方法比仅仅评级的方法提供了显著的优势,因为它更准确地提炼了审查员的共识.
- 拟议的方法是可通用的,适用于各种同行评审设置,包括国家卫生研究院 (NIH) 的同行评审设置.
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