The Food and Nutrition Policy Environment and Drivers of Changes in Key Food System Outcomes in Ethiopia
Bibliographic record
Abstract
Background: There seems to be huge gap in our understanding of the changes over time in food system outcomes and their drivers in Ethiopia. The main aim of this study is to examine the food and nutrition programs and policies and their corresponding key food system outcomes in Ethiopia. Methods: The bulk of the information was generated using scooping review of relevant articles and policy documents. About 67 full text records were used for the review. In addition, data were collected using Key Informant Interview (KIIs) purposefully selected from sectoral offices from two major cities (Hawassa and Dire Dawa), of two regions. The analytical framework used in this paper was adopted from previous studies on related subjects and addressed three key components of food system: review of food and nutrition policy environment, key food system outcomes and key drivers. Results: Despite improvement in some food system outcomes (such as child nutrition and survival), food security crises in Ethiopia are still becoming more frequent and more acute, affecting the poor disproportionately. Most food and nutrition policies are constrained by lack of implementation capacities. Indicating the presence of various barriers (socioeconomic, demographic, and environmental). Poor human capital (such as knowledge and attitude), food taboos and tradition, cultural practices such as gender-based norms, poor education, poor delivery/supply chain, demographic pressure and other environmental drivers play critical role in food and nutrition security of most vulnerable population groups in Ethiopia. Conclusion and implications: Given the challenges confronting Ethiopia today, it is imperative to assume that meeting Sustainable Development Goal (SDG) 2 (i.e., attaining zero hunger by 2030) becomes challenging. This calls for continuous capacity building to help implement, learn, and adapt a systems approach; and access to education and skill training on food production and consumption and narrowing down the gender differential in food access and consumption.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".