Threats to grant peer review: a qualitative study
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Peer review is ubiquitous in evaluating scientific research. While peer review of manuscripts submitted to journals has been widely studied, there has been relatively less attention paid to peer review of grant applications (despite how crucial peer review is to researchers having the means and capacity to conduct research). There is spirited debate in academic community forums (including on social media) about the perceived benefits and limitations of grant peer review. The aim of our study was to understand the experiences and challenges faced by grant peer reviewers. METHODS: Therefore, we conducted qualitative interviews with 18 members of grant review panels-the Chairs, peer reviewers and Scientific Officers of a national funding agency-that highlight threats to the integrity of grant peer review. RESULTS: We identified three threats: (1) lack of training and limited opportunities to learn, (2) challenges in differentiating and rating applications of similar strength, and (3) reviewers weighting reputations and relationships in the review process to differentiate grant applications of a similar strength. These threats were compounded by reviewers' stretched resources or lack of time. Our data also highlighted the essential role of the Chair in ensuring transparency and rigorous grant peer review. CONCLUSIONS: As researchers continue to evaluate the threats to grant peer review, the reality of stretched resources and time must be considered. We call on funders and academic institutions to implement practices that reduce reviewer burden.
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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 | 0.099 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".