How can agricultural interventions enhance contribution to food security and SDG 2.1?
Why this work is in the frame
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Bibliographic record
Abstract
The Sustainable Development Goals (SDGs), and specifically SDG 2, commit the international community to achieve zero hunger by 2030 through a renewed focus on agricultural development for food security and nutrition. This paper presents a systematic review of published evidence of contributions by agricultural sector interventions to food security, including indicators and success factors. Our literature screening yielded a sample of 66 publications with 73 single or multiple intervention evaluations. Of these, 38 (52%) used a direct food security indicator to measure food security impacts, and the rest used a proxy indicator. Of the 73 evaluations, 49 (67%) resulted in positive impacts on food security, 17 (23%) produced no measurable impacts and 7 (10%) led to negative impacts. Interventions studied included input subsidies, extension services and value chain enhancements, delivered either alone or together, and sometimes with specific complementary pro-poor features. Studies showed positive, neutral or even negative food security outcomes across all intervention types, suggesting that program design features may be more important than the type of intervention in determining impact on food security. Positive food security outcomes were shown to be linked to multiple complementary interventions, targeted pro-poor support features, responsiveness to local food security issues, community engagement and collaboration with local and regional institutions. We suggest methods for improving monitoring, evaluation and learning related to the food security impacts of agriculture sector interventions in order to contribute to SDG 2.1.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it