Household Food Insecurity and Hunger in Selected Ethiopian Agricultural Communities: Examination of Supply and Demand Factors
Bibliographic record
Abstract
Food insecurity and hunger are major challenges in many Ethiopian communities with repercussions on health and nutrition outcomes in vulnerable household members. The level and contextual risk factors of household food insecurity and hunger were assessed in households (n=630) from three rural communities of Ethiopia (Halaba or Zeway) using the Household Food Insecurity Access Scale and Household Hunger Scale. Multiple classification analysis was employed to explore the effects of key demand (e.g. household size, livestock) and supply (e.g. land size, frequency of production) factors and community (geographic location) as well as institutional (participation in food security programs) factors on food insecurity and hunger. Household food insecurity was unacceptably high in both districts (95% in Halaba & 67% in Zeway). Household hunger was 38% in Halaba and 18% in Zeway. Both food insecurity and hunger were significantly greater in Halaba (p<0.001), indicating an effect of geographic location. Both supply and demand factors were significant in determining household food insecurity and hunger (p<0.01); however, supply factors such as women’s access to land, land size and wealth had greater influence than the demand factors. Levels of food insecurity and hunger in both communities were very high and of serious concern. We recommend increasing the food supply, and its subsequent accessibility, for households through enhancing women’s access to land, improving income through savings and wealth accumulation, introducing more inclusive programs for women’s participation and reducing household work-burden by significantly enhancing productivity of cultivable land.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".