South‐South Migration and Urban Food Security: Zimbabwean Migrants in South African Cities
Bibliographic record
Abstract
Abstract The drivers of food insecurity in rapidly‐growing urban areas of the Global South are receiving more research and policy attention, but the precise connections between urbanization and urban food security are still largely unexplored. In particular, the levels and causes of food insecurity amongst new migrants to the city have received little consideration. This is in marked contrast to the literature on the food security experience of new immigrants from the South in European and North American cities. This article aims to contribute to the new literature on South‐South migration and urban food security by focusing on the case of recent Zimbabwean migrants to South African cities. The article presents the results of a household survey of migrants in the South African cities of Cape Town and Johannesburg. The survey showed extremely high levels of food insecurity and low dietary diversity. We attribute these findings, in part, to the difficulties of accessing regular incomes and the other demands on household income. However, most migrants are also members of multi‐spatial households and have obligations to support household members in Zimbabwe. We conclude, therefore, that although migration may improve the food security of the multi‐spatial household as a whole, it is also a factor in explaining the high levels of insecurity of migrants in the city.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".