Climate Change and Food Security in Sub-Saharan Africa: A Systematic Literature Review
Bibliographic record
Abstract
In recent years it has become clear that climate change is an inevitable process. In Sub-Saharan Africa, the expectation is that climate change will have an especially negative impact, not only a result of projected warming and rainfall deficits, but also because of the vulnerability of the population. The impact upon food security will be of great significance, and may be defined as being composed of three components: availability, access, and utilization. To further investigate the link, a systematic literature review was done of the peer-reviewed literature related to climate change and food security, employing the realist review method. Analysis of the literature found consistent predictions of decreased crop productivity, land degradation, high market prices, negative impacts on livelihoods, and increased malnutrition. Adaptation strategies were heavily discussed as a means of mitigating a situation of severe food insecurity across the entire region. This is linked to issues of development, whereby adaptation is essential to counteract the negative impacts and improve the potential of the population to undergo development processes. Findings additionally revealed a gap in the literature about how nutrition will be affected, which is of importance given the links between poor nutrition and lack of productivity.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".