Knowledge Translation in the Global South: Bridging Different Ways of Knowing for Equitable Development
Bibliographic record
Abstract
This study explores knowledge translation (KT) in the global South and provides recommendations for funders to support more effective structures and strategies for the use of research for equitable development. The project explores the KT strategies, practices and theories researchers and research intermediaries use in the global South, and the challenges they experience, and identifies the types of support required from research funders. The mixed methods design incorporated facilitated learning sessions, a review of the literature, case study selection and analysis, and semi-structured interviews. The research finds that KT is too narrowly defined and a holistic approach is needed support it in the global South. Recommendations for funders include creating challenge funds, taking a programme-level approach to supporting KT, and embracing complexity.
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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 | 0.054 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.008 | 0.032 |
| Scholarly communication | 0.023 | 0.029 |
| Open science | 0.002 | 0.034 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 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".