Positioning Research for Impact: Lessons From a Funder During the Covid-19 Pandemic
Bibliographic record
Abstract
The Covid-19 pandemic has reinforced the value of robust, policy-relevant research to inform decision-making and heightened the need for evidence-informed responses to address worsening inequalities. While international development research has the potential to contribute to a more equitable world, research funders grapple with how to ensure that their support best enables researchers to respond to evolving evidence demands and influence policy and practice. This article reflects on lessons emerging from one of the International Development Research Centre’s (IDRC) rapid-response initiatives and highlights the ongoing experiences of our research partners in influencing policy to address the socioeconomic impacts of the pandemic. We conclude that flexibility of funding, promoting Southern leadership and embedded partnerships, and ongoing support for amplification of research results help to ensure that research is positioned for impact amid constantly evolving priorities. This has implications for research funding practices and underlines the importance of addressing inequities in access to research funding.
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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 | 0.219 | 0.264 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.021 | 0.030 |
| Scholarly communication | 0.037 | 0.029 |
| Open science | 0.005 | 0.026 |
| Research integrity | 0.024 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".