Linking African smallholders to high-value markets : practitioner perspectives on benefits, constraints, and interventions
Bibliographic record
Abstract
This paper provides the results of an international survey of practitioners with experience in facilitating the participation of African smallholder farmers in supply chains for higher-value and/or differentiated agricultural products. It explores their perceptions about the constraints inhibiting and the impacts associated with this supply chain participation. It also examines their perceptions about the factors affecting the success of project and policy interventions in this area, about how this success is and should be measured, and about the appropriate roles for national governments, the private sector, and development assistance entities in facilitating smallholder gains in this area. The results confirm a growing'consensus'about institutional roles, yet suggest some ambiguity regarding the impacts of smallholder participation in higher-value supply chains and the appropriateness of the indicators most commonly used to gauge such impacts. The results also suggest a need to strengthen knowledge about both the'old'and'new'sets of constraints (and solutions) related to remunerative smallholder inclusion, in the form of the rising role of standards alongside more long-standing concerns about infrastructure and logistical links to markets.
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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 | 0.025 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".