The Africa Food Environment Research Network (FERN): from concept to practice
Bibliographic record
Abstract
BACKGROUND: Africa is contending with unhealthy food environments that are, in part, driving increasing rates of overweight, obesity and diet-related non-communicable diseases, alongside persistent undernutrition. This current paradigm requires expanded efforts - both in the volume and nature of empirical research, as well as the tools and capacity of those who conduct it. High quality and context-relevant research supports the development and implementation of policies that create healthy food environments. AIM AND APPROACH: This paper sets out the concept of the Africa Food Environment Research Network (FERN) initiative recently established by the Measurement, Evaluation, Accountability, and Leadership Support for non-communicable diseases (NCDs) (MEALS4NCDs) prevention project. Central to the Africa FERN initiative are: 1) building research capacity for innovative food environment research in Africa; 2) improving South-South, South-North partnerships to stimulate robust food environment research and monitoring in Africa and 3) sustaining dialogue and focusing priorities around current and future needs for enhanced food environment research and monitoring in Africa. CONCLUSION: The FERN initiative presents an opportune platform for researchers in Africa and the global North to weave the threads of experience and expertise for research capacity building, collaboration and advocacy, to advance food environment research.
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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 | 0.182 | 0.149 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.020 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".