Streamlining Grant Applications - What are the probabilities a streamlined grant is fundable and that a fundable grant is streamlined?
Bibliographic record
Abstract
Background: Securing qualified peer reviewers for public granting agencies is challenging and to avoid needlessly overworking these volunteers, there is increasing reliance on triaging grants deemed unlikely to be competitive. For example, Canadian Institute for Health Research (CIHR), our largest public funder, has a stream-lining process whereby if the average of three initial reviewer scores is < 4.0, the submission is automatically triaged.Methods: Using Bayesian methods to account for the uncertainty in the re-viewers’ score, small sample sizes and vague prior beliefs, the true mean posterior(updated) distribution of a streamlined grant was calculated, including the probability it exceeds the threshold. The predictive distribution a new single reviewer, or a group of reviewers, might accord the streamlined grant while incorporating the aforementioned uncertainties was also calculated. Finally, the probability a submission with a true value > 4 could be streamlined was also calculated.Results: Simulations suggest that grants with mean scores slightly below 4 may have significant probabilities (30-50%) of being fundable, when reviewer variability is high. The mean probability that a new reviewer would rate a previously streamlined grant with a score > 4 was 48% (95% confidence interval (CI)0.13 - 0.86). The average probability that 15 new reviewers would rate the sestreamlined grants > 4 was 44% (95% CI 7 - 93). Simulations suggest that even streamlined grants with scores of 3.7 or 3.8, if associated with high reviewer variations, may have at least a 30% probability that the next reviewer score is > 4. Simulations also show that fundable grants, those with true scores in the range of 4.1 - 4.3, have a 20-40% probability of being streamlined.Conclusions: This study emphasizes the need to reevaluate streamlining mechanisms to increase the fairness and accuracy of the evaluation process. Agencies should consider adjusting thresholds or requiring additional reviews for streamlined applications with high variability to avoid excluding potentially fundable grants from full committee consideration.
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 | 0.169 | 0.599 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".