How and why funders support engaged research
Bibliographic record
Abstract
Research that better aligns policy, practice, and research communities is gaining momentum around the world. This includes engaged research strategies that bring partners, and their diverse perspectives and kinds of knowledge, together to shape research agendas with on-the-ground-needs and to create dynamic problem-solving processes. These approaches aim to generate more equitable and effective solutions to societal challenges. Although many of these partnered strategies have a longstanding history, entrenched research cultures, practices, and institutional structures stand in the way of scaling them. Given the outsized role funders play in shaping research efforts, funders are a critical lever for change. This perspective describes the efforts of a global collaborative of philanthropic and public funders who are adapting their practices, supporting the development of infrastructure (e.g., capacity-strengthening, facilitation expertise, processes to guide relational work, etc.), and targeting system-level challenges to enable engaged research to maximize its potential. The authors integrate insights from different issue areas, geographies, and funding areas to provide concrete examples of funder activities that support engaged research and to suggest areas for further action. Recommendations include scaling changes in funding practices, deepening understanding of how and when engaged research leads to improved outcomes, and reshaping how success is defined and measured.
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 | 0.122 | 0.328 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.025 |
| Scholarly communication | 0.049 | 0.030 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.018 | 0.010 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".