Dynamics of Rural-Urban Food Systems in Southern Africa
Bibliographic record
Abstract
Millions of households in Africa sit at the confluence of rapid urbanization and challenging climate conditions - making them especially vulnerable to food insecurity. Food systems in Africa must provide for an increasing number of urban residents, whilst coping with the impacts of climate variability and change. As urbanization processes continue to intensify, the connectivity between rural and urban areas is particularly important for both food and livelihood security yet remains critically understudied. Secondary urban areas are of particular concern, as they outnumber large cities in both number and cumulative population but are often marginalized in research and policy. These smaller urban areas are often the most tightly connected to rural agricultural production, in proximity and their vulnerability to agricultural shocks. How food systems operate across the rural-urban continuum is of central importance for food and livelihood security. Broadly, this dissertation examines these dynamics, seeking to understand how climate shocks in rural agroecosystems impact urban food security, as well as the reciprocal processes through which urban growth and urban food demand alter the function of rural-urban food systems. This work focuses on rapidly urbanizing secondary urban areas in southern Africa, which represent 85% of the total urban population in the region. The goals of this dissertation are threefold: 1) Quantify the patterns and drivers of urban population growth in secondary urban areas; 2) Examine the relationships between climate, geographic, and population-based variables, and food price volatility in urban areas and 3) Identify important rural-urban linkages and household food sourcing strategies used to cope with food price volatility in secondary urban areas. Across southern Africa, communities and nations are struggling to meet increasing food demand across a range of spatial scales. This dissertation highlights the need for solutions to cope with the impacts of climate change and rapid urbanization. More than half of the global population currently lives in urban areas, and the functioning of rural-urban food systems is of critical importance to ensure sustainable development and food security.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".