‘A foreigner is not a person in this country’: xenophobia and the informal sector in South Africa’s secondary cities
Bibliographic record
Abstract
South Africa's major cities are periodically wracked by large-scale xenophobic violence directed at migrants and refugees from other countries. Informal sector businesses and their migrant owners and employees are particularly vulnerable targets during these attacks. Migrant-owned businesses are also targeted on a regular basis in smaller-scale looting and destruction of property. There is now a large literature on the characteristics and causes of xenophobic violence and attitudes in South Africa, most of it based on quantitative and qualitative research in the country's major metropolitan areas. One of the consequences of big-city xenophobia has been a search for alternative markets and safer spaces by migrants, including relocating to the country's many smaller urban centres. The question addressed in this paper is whether they are welcomed in these cities and towns or subject to the same kinds of victimization as in large cities. This paper is the first to systematically examine this question by focusing on a group of towns in Limpopo Province and the experiences of migrants in the informal sector there. Through survey evidence and in-depth interviews and focus groups with migrant and South African vendors, the paper demonstrates that xenophobia is also pervasive in these smaller centres, in ways that both echo and differ from that in the large cities. The findings in this paper have broader significance for other countries attempting to deal with the rise of xenophobia.
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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 | 0.011 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".