Promoting Transparent, Fair, and Inclusive Practices in Grantmaking: Lessons from the Open and Equitable Model Funding Program
Bibliographic record
Abstract
This report presents the insights of the Open & Equitable Model Funding Program, a pilot of a cohort of eleven research funders interested in refining their grantmaking to foster open and equitable practices. Launched in April 2021 by the Open Research Funders Group (ORFG) with grants ranging from $5 to $560 million, this initiative brought together experts across various fields to create thirty-two interventions to promote open research and equitable grantmaking. The funders cohort fostered a collaborative learning environment through monthly meetings, allowing participants to share insights and tackle challenges. Supported by the ORFG's resources and guidance, this structured approach facilitated the tailoring of interventions to each funder's specific needs, emphasizing early identification of challenges to integrate these practices seamlessly into existing funding mechanisms. Despite facing challenges such as staff turnover, limited time, and resources, which impacted the full engagement with and implementation of the interventions, the pilot was appreciated for its organized and guided framework and its collaborative learning environment. Participants who met their pilot goals attributed their success to the clear, achievable interventions and the structured design of the pilot, which allowed for focused implementation and executive-level support. The initiative also encouraged collaboration among peers, fostering a community of like-minded organizations exploring common challenges. The ORFG's documentation of lessons learned and the testing of intervention suitability offers valuable insights for future funders to refine their grantmaking strategies, underscoring the importance of continuous effort and commitment to achieve lasting change. These recommendations were refined for relevance and completeness from direct engagement with applicants, grantees, and researchers from underserved communities, ensuring the incorporation of insights from historically marginalized groups and with the goal of tailoring more inclusive and practical improvements.
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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 | 0.156 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.016 | 0.019 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.004 | 0.034 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".