Climate change, food security, and livelihoods in sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa is particularly vulnerable to climate change. Multiple biophysical, political, and socioeconomic stresses interact to increase the region’s susceptibility and constrain its adaptive capacity. Climate change is commonly recognized as a major issue likely to have negative consequences on food security and livelihoods in the region. This paper reviews three bodies of scholarship that have evolved somewhat separately, yet are inherently interconnected: climate change impacts, vulnerability and adaptation, food security, and sustainable livelihoods. The paper develops a conceptualization of the relationships among the three themes and shows how food security’s vulnerabilities are related to multiple stresses and adaptive capacities, reflecting access to assets. Food security represents one of several livelihood outcomes. The framework shows how several research paradigms relate to the issue of food security and climate change and provides a guide for empirical investigations. Recognizing these interconnections can help in the development of more effective policies and programs. The framework is applied here to synthesize findings from an array of studies in sub-Saharan Africa dealing with vulnerability to climate change, food security, and livelihoods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".