Food Insecurity and Labour Migration from Northern Malawi to South Africa
Bibliographic record
Abstract
Abstract This chapter explores the intricate relationship between food insecurity and labour migration from Northern Malawi to South Africa, highlighting a historical context of migration that dates back to the late nineteenth century. The focus is on the contemporary migration dynamics from the Mzuzu area in Northern Malawi, examining the decision-making factors for migration, the migration process and the experiences of migrants in transit and at their destination. The study reveals that food insecurity, coupled with unemployment, low income and the pursuit of better opportunities, primarily drives individuals to migrate. Using qualitative research methods, including interviews with migrant-sending households and returnee migrants, the chapter delves into the socioeconomic impacts of migration, such as improved livelihoods through remittances that facilitate access to food, education and housing. However, the journey to South Africa, often characterised by irregular migration routes, poses significant risks, including exposure to exploitation and violence. The chapter underscores the need for a nuanced understanding of migration’s role in addressing food insecurity and its broader socioeconomic implications for the migrants and their families in Malawi.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".