Linking African smallholders to high-value markets : practitioner perspectives on benefits, constraints, and interventions
Bibliographic record
Abstract
This paper provides the results of an \n international survey of practitioners with experience in \n facilitating the participation of African smallholder \n farmers in supply chains for higher-value and/or \n differentiated agricultural products. It explores their \n perceptions about the constraints inhibiting and the impacts \n associated with this supply chain participation. It also \n examines their perceptions about the factors affecting the \n success of project and policy interventions in this area, \n about how this success is and should be measured, and about \n the appropriate roles for national governments, the private \n sector, and development assistance entities in facilitating \n smallholder gains in this area. The results confirm a \n growing 'consensus' about institutional roles, yet \n suggest some ambiguity regarding the impacts of smallholder \n participation in higher-value supply chains and the \n appropriateness of the indicators most commonly used to \n gauge such impacts. The results also suggest a need to \n strengthen knowledge about both the 'old' and \n 'new' sets of constraints (and solutions) related \n to remunerative smallholder inclusion, in the form of the \n rising role of standards alongside more long-standing \n concerns about infrastructure and logistical links to markets.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".