No. 23: The Food Insecurities of Zimbabwean Migrants in Urban South Africa
Bibliographic record
Abstract
This report examines the food security status of Zimbabwean migrant households in the poorer areas of two major South African cities, Johannesburg and Cape Town. The vast majority were food insecure in terms of the amount of food to which they had access and the quality and diversity of their diet. What seems clear is that Zimbabwean migrants are significantly more food insecure than other low-income households. The primary reason for this appears to lie in pressures that include remittances of cash and goods back to family in Zimbabwe. The small literature on the impact of migrant remittances on food security tends to look only at the recipients and how their situation is improved. It does not look at the impact of remitting on those who send remittances. Most Zimbabwean migrants in South Africa feel a strong obligation to remit, but to do so they must make choices because of their limited and unpredictable income. Food is one of the first things to be sacrificed. Quantities decline, cheaper foods are preferred, and dietary quality and diversity inevitably suffer. This study found that while migrants were dissatisfied with the shrinking job market in South Africa, most felt that they would be unlikely to find work in Zimbabwe and that a return would worsen their household’s food security situation. In other words, while food insecurity in Zimbabwe is a major driver of migration to South Africa, food insecurity in South Africa is unlikely to encourage many to return.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".