Malnutrition and food insecurity in northern Nigeria: an insight into the United Nations World Food Program (WFP) in Nigeria
Bibliographic record
Abstract
Malnutrition and food insecurity are two major diseases combating human development in Nigeria as they cause poor infant development, deteriorating maternal and child health, weaker immune systems, risky pregnancy and childbirth. This paper provides an in-depth overview of the implementation, impact, benefits and costs of the United Nations World Food Programme (WFP), in relation to malnutrition and food insecurity, in northern Nigeria. The primary purpose of the WFP is to meet the immediate food and nutrition needs of those most exposed to acute hunger. However, some of the challenges include conflict and insecurity, rising inflation and the impact of climate crisis. These challenges are been tackled through works at the humanitarian-development-peace nexus, with targeted emergency responses with a view to a sustainable food security for all, which aligns with various partnerships and collaborations. With a comprehensive approach that spans emergency response, long-term development, and humanitarian services through supplementary feeding, nutrition education and capacity building, the WFP has made a significant impact in improving the lives of millions of Nigerians facing the challenges of poverty, conflict, and climate change. Although, there was an increase in food inflation and under-nourishment in Nigeria, the operations of the WFP was positive and significant in contributing to human development in northern Nigeria. It is recommended that the WFP, in collaboration with the government, private sector and other humanitarian agencies, provide a more robust and holistic food assistance and skills development to hunger prone areas in Nigeria. Furthermore, the WEP should provide the necessary support for food production in Nigeria through youth-inclusive and reliable marketing strategies in rural 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".