Exploring African perspectives on food system leadership : Two cases from Malawi and Cameroon, commissioned by the African Food Fellowship
Bibliographic record
Abstract
The African Food Fellowship (AFF) is engaging with partners to better understand African perspectives on leadership in food systems. This includes the forms that leadership networks and systems take across the continent, as well as how these can contribute to sustainable food systems change. The result is a series of case studies to explore food system leadership in Africa, demonstrating how collective food systems leadership is manifested and what outcomes are achieved. A particular focus of the case studies is: -The characteristics of leadership networks that facilitate shifts in policies, power dynamics and incentives toward food system transformation. -The capabilities of leadership networks that support shifts towards sustainable and inclusive food systems.This case study series presents two AFF-commissioned case studies exploring African perspectives on collective food system leadership in Malawi and Cameroon. The case studies in this report were developed by African Projects Solutions (APS) in partnership with the Small Five Knowledge Collective. The two case studies demonstrate how collective food systems leadership is manifested and what outcomes can be (or have been) achieved. The first case study is on a southern African organisation – Soils, Food and Healthy Communities (SFHC) – based in Malawi and the second is on Concertation Nationale des Organisations Paysannes au Cameroun (CNOP-CAM, translated in English to the National Federation of Peasants’ Organisationsof Cameroon) based in central Africa.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".