Impact of Agricultural Diversification and Commercialization on Child Nutrition in Zambia: A Dose Response Analysis
Bibliographic record
Abstract
Zambia, and in particular Eastern Province, has one of the highest levels of malnutrition in the world with 40% of the children having stunted growth. Agricultural diversification and commercialization remain critical for improving the nutrition status of children. However, the impact may vary according to the level of the two agricultural interventions. Results from the dose response function using generalised propensity score method showed that for commercialization, there is highest risk of stunting at medium commercialization levels at 50%. A farm at this point can improve nutrition status by moving either towards high or towards zero levels. Commercialization has a negative effect on short-term nutrition outcomes leading to underweight and wasting. This could indicate that in areas with less everyday access to a range of food items, capital accumulation may not help to avoid deficiencies in child nutrition. In combination with our findings on diversification, two policy options can be recommended. Either the households specialize in cash crops to increase income, or they go into subsistence farming with high levels of diversification. Other off-farm income sources are suggested for resilience in case of yield shocks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".