Cross-sector learning among researchers and policy-makers: the search for new strategies to enable use of research results
Bibliographic record
Abstract
This paper assesses the preliminary results of a research funding strategy that alters the structure and process of research by requiring interaction between researchers and policy-makers. The five research teams focused on different aspects of expanding social protection in health in Latin America and the Caribbean. Preliminary results revealed negotiation of the research questions at the start of the process, influencing not only the project design, but the decision-makers' ways of thinking about the problem as well. As the projects advanced, turnover among government officials on four of the teams impaired the process. However, for the one team that escaped re-composition, the interaction has led to use of data in decision-making, as well as a clear recognition by both parties that different kinds of evidence were at play. The process highlighted the importance of stimulating systems of learning in which multiple kinds of knowledge interact. This interaction may be a more realistic expectation of such initiatives than the original goal of "transferring" research knowledge to policy and practice.
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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 | 0.587 | 0.520 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.019 | 0.033 |
| Scholarly communication | 0.048 | 0.048 |
| Open science | 0.007 | 0.071 |
| Research integrity | 0.015 | 0.014 |
| Insufficient payload (model declined to judge) | 0.011 | 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".