How can funders promote the use of research? Three converging views on relational research
Bibliographic record
Abstract
Abstract Although funders are generally acknowledged as important actors in the evidence ecosystem, there has been insufficient analysis of thehowandwhybehind funders’ decisions. This article examines the decision-making of three funders in their support of relational approaches to improve the usefulness and use of research evidence. They compare their work across the disparate policy sectors of education and environmental sustainability in order to bridge the silos that have caused unnecessary duplication of work and obstructed advancements in research utilization. The authors (1) provide individual narratives of their funding experiences including why they prioritized relational approaches and how they supported them; (2) discuss their lessons learned for supporting and promoting relational approaches; and (3) offer recommendations to the broader funding community for strengthening and expanding these approaches. The authors hope the paper provides useful insights into ways funders and their partners can build a stronger and better coordinated evidence ecosystem in which research regularly contributes to improved societal outcomes.
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 | 0.381 | 0.394 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.020 | 0.097 |
| Scholarly communication | 0.078 | 0.041 |
| Open science | 0.006 | 0.054 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".