EVIPNet Africa's first series of policy briefs to support evidence-informed policymaking
Bibliographic record
Abstract
EVIPNet (Evidence-Informed Policy Network) Africa—a network of World Health Organization (WHO)-sponsored knowledge-translation (KT) platforms in seven sub-Saharan African countries—was launched at a meeting in Brazzaville, Congo, in March 2006 (1;2). EVIPNet Africa can trace its origins to resolutions from both the Ministerial Summit on Health Research (November 2004) and the World Health Assembly (May 2005) (10;11), the spirit of which was re-affirmed at the Global Ministerial Forum on Research for Health (November 2008) (13). The World Health Assembly called for “establishing or strengthening mechanisms to transfer knowledge in support of evidence-based public health and health care delivery systems and evidence-based related policies” (10). EVIPNet Africa can trace its inspiration to a more local development: the preparatory work that led to the establishment of the East African Community–sponsored Regional East African Community Health (REACH) Policy initiative, a KT platform involving Kenya, Tanzania, and Uganda (and more recently Burundi and Rwanda as well). REACH Policy is now part of the EVIPNet Africa family.
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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 | 0.072 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.027 | 0.021 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".