West Africans in the Informal Economy of South Africa: The Case of Low Skilled Nigerian Migrants
Bibliographic record
Abstract
Economic issues are typically at the heart of migration decisions globally. Disparities in incomes between countries play a major role in motivating people with different levels of skills to migrate from Africa to countries with more developed economies in search of secure livelihoods and improved quality of life. South Africa, an upper middle income country, is the leading migration destination country in sub-Saharan Africa because of its buoyant economy. Research has focused on cross-border migration to South Africa while less attention has been on migration among low-skilled migrants from West Africa. We conducted an ethnographic study of 13 low-skilled Nigerian migrants working as street traders in a flea market in Durban. South Africa. Findings indicate that migrants were motivated to travel to South Africa because of misinformation from migrant returnees as well as friends and family resident in South Africa about their potential for considerable economic success abroad. Some were stuck while planning to use South Africa as a transit country to other countries in Europe and North America. Migrants encountered many challenges that prevented them from achieving their dreams of living a better life overseas and ended up as street traders in the flea market. There they endured a lesser quality of life than in their home country but were ashamed to return home preferring instead to eek out a living, and to request and receive financial support from their families at home. Findings have implications for migration policy highlighting the need for innovative ways of disseminating accurate information to potential migrants and assisting migrants to return home. Further research on migrant integration to host country and reverse remittances will be of great value.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".