Case Study: Using AI to Design More Effective, Efficient, and Equity Focused Grant Application Review Processes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Grants fuel millions of organizations and advance social innovation across sectors. In the United States, foundations and corporations respectively awarded $103.5B and $36.5B to nonprofits in 2023 alone (Childress, 2024; Lilly Family School of Philanthropy, 2024). By extension, grant applications play a critical role in how grant recipients are identified and selected. While many funders are eager for more streamlined and equitable processes to review grant applications, the process remains primarily a manual and time consuming one (Hewlett Foundation, 2021). These factors leave some funders implementing grant application processes potentially at odds with their impact goals. Examples include restricting who can apply, including only application reviewers with specific credentials, or not screening for inconsistencies among reviewer assessments (Candid., n.d.). In this article, readers will learn how Missouri Foundation for Health and AI PRIORI® used artificial intelligence to inform a more effective, efficient, and equity focused application review process. Readers will gain insights into the experiments, AI tools used, and preparation and evaluation of data sets.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.108 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it