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Record W4409180299 · doi:10.1007/978-981-97-9715-8_2

Food Insecurity and Labour Migration from Northern Malawi to South Africa

2025· book-chapter· en· W4409180299 on OpenAlexaff
Anil Dhakal

Bibliographic record

VenueInternational perspectives on migration · 2025
Typebook-chapter
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsTed Rogers Centre for Heart Research
Fundersnot available
KeywordsFood insecurityGeographyFood securityDevelopment economicsSocioeconomicsEconomicsAgricultureArchaeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.905
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.016
GPT teacher head0.270
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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