Challenges to groundnut value chain development: lessons from an (attempted) experiment in Ghana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In developing countries, value chains for many crops are underdeveloped, leading to low producer prices and poor quality produce. Value chain research using secondary data is made difficult by selection problems, whereas experimental research is logistically very difficult and lacks external validity. With the intention of conducting a field experiment, we piloted an intervention connecting smallholder groundnut farmers in Ghana to a premium groundnut processor through aggregators. While we successfully delivered inputs and training to farmers, we failed in our attempts to link aggregators with downstream processors over two growing seasons. In this paper, we situate the challenges we faced in the broader literature on value chains and identify three problems that prevented us from establishing a value chain for high quality groundnuts: uncertainty, cash constraints, and trust. To help inform future research on this topic, we propose three specific interventions that could mitigate these problems.
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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 | 0.000 | 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 it