The Impact of Xenophobic Attacks on the Livelihoods of Migrants in Selected Provinces of South Africa
Bibliographic record
Abstract
Migration and xenophobia are realities that cannot be ignored globally. Recently, there has been a plethora of xenophobic attacks as a result of structural and socio-economic conditions in South Africa. This paper aimed to establish the impact of xenophobic attacks on the livelihoods of migrants in selected provinces of South Africa. Researchers opted for a qualitative study using a case study design. Participants were drawn from the population in Limpopo, North-West and Mpumalanga Province. A snowball sampling technique was used to sample seven migrants from Zimbabwe, India, and Ethiopia using semi-structured interviews. Data were analysed thematically. The study revealed that most migrants who reside in the rural areas of South Africa seldom experience xenophobic attacks, and therefore their livelihoods are not always negatively affected. The study concluded that displacement, deportation, and loss of income due to xenophobic attacks are experiences of undocumented migrants in the cities and not in the rural areas. This study also makes recommendations that migration management policies be implemented fully in the rural areas because this is where undocumented migrants find comfort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".