Implementing small‐scale poultry‐for‐nutrition projects: Successes and lessons learned
Bibliographic record
Abstract
This paper examines Helen Keller International's model for nutrition-sensitive poultry production using a programme implemented in four diverse African contexts-three rural and one urban. Consecutive cross-sectional surveys conducted every 5 months among ~15% of participating households show that despite project-provided training and inputs, there was only limited uptake of many "best practices." Few households constructed improved henhouses; vaccination rates varied and were highest when support was provided. Poultry mortality was high. Egg productivity remained average for village poultry systems, and egg consumption remained low (two to six eggs consumed per household per fortnight). However, children whose mothers were exposed to project messages on nutrition were more likely to eat eggs, and consumption was consistently higher among households with chickens. Women's involvement in chicken rearing was widespread, but their control over revenues from the sale of poultry products was limited. Key lessons learned from implementation were as follows: (a) strong behaviour change communication is needed to encourage egg consumption, (b) nutrition-sensitive village poultry programmes should often focus more on improved practices than improved breeds, (c) supporting women's chicken production is not a route to empowerment without complementary activities that directly support women's ownership and decision making. There is also a need for rigorous research on the role of village poultry in livelihoods, food systems, and consumption as well as the structure of poultry and egg markets in low-resource areas.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".