Abstract
The process of evaluating research proposals for funding is often based on subjective assessments of the "goodness" or "badness" of a proposal. However, this method of evaluation is not precise and does not provide a common language for reviewers to communicate with each other. In this paper, we propose that science funding agencies ask reviewers to assign quantitative probabilities to the likelihood of a proposal reaching a particular milestone or achieving technical goals. This approach would encourage reviewers to be more precise in their evaluations and could improve both agency-wide and individual reviewer calibration over time. Additionally, this method would allow funding agencies to identify skilled reviewers and allow reviewers to improve their own performance through consistent feedback. While this method may not be suitable for all types of research, it has the potential to enhance proposal review in a variety of fields. [abstract generated by ChatGPT]
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