Assessing the Zero Hunger Target Readiness in Africa in the Face of COVID-19 Pandemic
Bibliographic record
Abstract
Sustainable Development Goal 2 (SDG 2) is hinged on achieving zero hunger target globally by 2030. Many developing countries, especially African countries, are challenged with extreme hunger that are often caused or compounded by bad governance, conflicts and climate change. In this paper, we assess Africa’s readiness towards attaining the zero hunger target by 2030 in the face of COVID-19 pandemic. Patterns of Global Hunger Index (GHI) and each of its indicators across Africa are compared before the pandemic (2000-2019). The effect of the pandemic on the hunger situation in Africa is discussed by highlighting the mitigating measures put in place by selected African governments. We have found that most African countries have recorded steady reduction in their child mortality rates but high prevalence of undernourishment, stunting and child wasting indicates significant challenges hampering the achievement of the zero hunger target. The study recommends that African governments should prioritize sustainable agricultural practices and give serious attention to the formulation and implementation of policies that reduce hunger against the COVID-19 pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".