What do we know about grant peer review in the health sciences?
Bibliographic record
Abstract
<ns4:p> <ns4:bold>Background</ns4:bold> : Peer review decisions award an estimated >95% of academic medical research funding, so it is crucial to understand how well they work and if they could be improved. </ns4:p> <ns4:p> <ns4:bold>Methods</ns4:bold> : This paper summarises evidence from 105 papers identified through a literature search on the effectiveness and burden of peer review for grant funding. </ns4:p> <ns4:p> <ns4:bold>Results</ns4:bold> : There is a remarkable paucity of evidence about the efficiency of peer review for funding allocation, given its centrality to the modern system of science. From the available evidence, we can identify some conclusions around the effectiveness and burden of peer review. </ns4:p> <ns4:p>The strongest evidence around effectiveness indicates a bias against innovative research. There is also fairly clear evidence that peer review is, at best, a weak predictor of future research performance, and that ratings vary considerably between reviewers. There is some evidence of age bias and cronyism.</ns4:p> <ns4:p>Good evidence shows that the burden of peer review is high and that around 75% of it falls on applicants. By contrast, many of the efforts to reduce burden are focused on funders and reviewers/panel members.</ns4:p> <ns4:p> <ns4:bold>Conclusions</ns4:bold> : We suggest funders should acknowledge, assess and analyse the uncertainty around peer review, even using reviewers’ uncertainty as an input to funding decisions. Funders could consider a lottery element in some parts of their funding allocation process, to reduce both burden and bias, and allow better evaluation of decision processes. Alternatively, the distribution of scores from different reviewers could be better utilised as a possible way to identify novel, innovative research. Above all, there is a need for open, transparent experimentation and evaluation of different ways to fund research. This also requires more openness across the wider scientific community to support such investigations, acknowledging the lack of evidence about the primacy of the current system and the impossibility of achieving perfection. </ns4:p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.310 | 0.771 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.018 |
| Science and technology studies | 0.007 | 0.027 |
| Scholarly communication | 0.030 | 0.045 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.017 | 0.022 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".