How Poverty Alleviation Efforts Manifest among Smallholder Groundnut Farmers in Eastern Zambia
Bibliographic record
Abstract
Poverty alleviation and health promotion programs have become part and parcel of life in rural Zambia. It is critical to track the performance of these programs to assess the impact they have on the people involved. The purpose of this study is to ascertain barriers, specifically related to market access and crop yields, faced by smallholder groundnut farmers in Eastern Zambia following implementation of the PROFIT+ program. Focus group discussion and informants were selected based on participation in the PROFIT+. Interview data were then qualitatively analyzed to determine consistent themes among farmers. Farmers highlight three general barriers/risks that impacted both their economic well-being and health. In some cases, these barriers may act as feedback loops, health affecting economic productivity and vice versa. These include (a) a lack of adequate storage facilities (b) exposure to aflatoxins produced by the Aspergillus fungus (c) and exposure to pesticides due to a lack of personal protective equipment. Generally, groundnut farmers have benefitted from the efforts of PROFIT+, though challenges remain. Farmers consistently report increased their crop yields; however, access to outside markets has yet to materialize. Exposure to both aflatoxins and pesticides are concerning, particularly in areas of high stunting rates as these chemicals may exacerbate the effects of malnutrition. Further, changing weather patterns in the context of climate change increase issues faces by smallholder farmers.
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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 | 0.001 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".