Bibliographic record
Abstract
Background: Evaluating applications for multi-national, multi-disciplinary, dual-purpose research consortia is highly complex. There has been little research on the peer review process for evaluating grant applications and almost none on how applications for multi-national consortia are reviewed. Overseas development investments are increasingly being channelled into international science consortia to generate high-quality research while simultaneously strengthening multi-disciplinary research capacity. We need a better understanding of how such decisions are made and their effectiveness. Methods: An award-making institution planned to fund 10 UK-Africa research consortia. Over two annual rounds, 34 out of 78 eligible applications were shortlisted and reviewed by at least five external reviewers before final selections were made by a face-to-face panel. We used an innovative approach involving structured, overt observations of award-making panel meetings and semi-structured interviews with panel members to explore how assessment criteria concerning research quality and capacity strengthening were applied during the peer review process. Data were coded and analysed using pre-designed matrices which incorporated categories relating to the assessment criteria. Results: In general the process was rigorous and well-managed. However, lack of clarity about differential weighting of criteria and variations in the panel’s understanding of research capacity strengthening resulted in some inconsistencies in use of the assessment criteria. Using the same panel for both rounds had advantages, in that during the second round consensus was achieved more quickly and the panel had increased focus on development aspects. Conclusion: Grant assessment panels for such complex research applications need to have topic- and context-specific expertise. They must also understand research capacity issues and have a flexible but equitable and transparent approach. This study has developed and tested an approach for evaluating the operation of such panels and has generated lessons that can promote coherence and transparency among grant-makers and ultimately make the award-making process more effective.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.121 |
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".