Scaling and institutionalization within agricultural innovation systems: the case of cocoa farmer field schools in Cameroon
Bibliographic record
Abstract
The farmer field school (FFS) concept has been widely adopted, and such schools have the reputation of strengthening farmers’ capacity to innovate. Although their impact has been studied widely, what is involved in their scaling and in their becoming an integral part of agricultural innovation systems has been studied much less. In the case of the Sustainable Tree Crops Programme in Cameroon, we investigate how a public–private partnership (PPP) did not lead to satisfactory widespread scaling in the cocoa innovation system. We build a detailed understanding of the key dimensions and dynamics involved and the wider lessons that might be learned regarding complex scaling processes in the context of agricultural innovation systems. Original interview data and document analysis inform the case study. A specific analytical approach was used to structure the broad-based exploration of the qualitative dataset. We conclude that scaling and institutionalization outcomes were impeded by: the lack of an adaptive approach to scaling the FFS curriculum, limited investments and genuine buy-in by extension actors, a failure to adapt the management approach between the pilot and the scaling phase, and the lack of strategic competencies to guide the process. Our findings support suggestions from recent literature that pilots need to be translated and adapted in light of specific contextual and institutional conditions, rather than approached as a linear rolling-out process. These findings are relevant for the further spread of similar approaches commonly involved in multi-stakeholder scaling processes such as innovation platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".