Assessing food insecurity strategies across twelve countries from different income levels: a sustainability and food systems perspective
Bibliographic record
Abstract
Abstract Achieving Sustainable Development Goal 2 (Zero Hunger) by 2030 remains a persistent global challenge, especially under current overlapping crises such as climate change, economic instability, and geopolitical conflicts. This study critically analyzes the food security strategies of twelve countries across four income groups, as classified by the World Bank: Low-Income (Malawi, Afghanistan, Ethiopia), Lower-Middle-Income (Nigeria, India, Lebanon), Upper-Middle-Income (Maldives, Brazil, China) and High-Income (Canada, Germany, United Arab Emirates). Using a structured narrative review of national policies and programs (2016–2024) sourced from academic databases, government publications, and international reports, we assess the alignment of strategies with the sustainability pillars (economic, social, environmental) and six key agri-food system interventions. Findings show that lower-income countries emphasize social protection and foundational agriculture (e.g., Ethiopia’s safety net improved food security by 30%), while higher-income nations focus on technological and environmental innovations (e.g., Germany aims to reduce nutrient losses by 50% by 2030). However, 10 of the 12 countries are off track, progressing at less than 50% of the rate needed. China (80% SDG2 score), Canada (70%), and Afghanistan (35%) demonstrate the widespread nature of this trend across varying income groups. The study underscores the urgency for integrated, context-specific strategies, enhanced international cooperation, and financing to accelerate progress toward Zero Hunger.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.003 |
| 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".