Ranking versus rating in peer review of research grant applications
Bibliographic record
Abstract
The allocation of public funds for research has been predominantly based on peer review where reviewers are asked to rate an application on some form of ordinal scale from poor to excellent. Poor reliability and bias of peer review rating has led funding agencies to experiment with different approaches to assess applications. In this study, we compared the reliability and potential sources of bias associated with application rating with those of application ranking in 3,156 applications to the Canadian Institutes of Health Research. Ranking was more reliable than rating and less susceptible to the characteristics of the review panel, such as level of expertise and experience, for both reliability and potential sources of bias. However, both rating and ranking penalized early career investigators and favoured older applicants. Sex bias was only evident for rating and only when the applicant's H-index was at the lower end of the H-index distribution. We conclude that when compared to rating, ranking provides a more reliable assessment of the quality of research applications, is not as influenced by reviewer expertise or experience, and is associated with fewer sources of bias. Research funding agencies should consider adopting ranking methods to improve the quality of funding decisions in health research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.410 | 0.727 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".