‘A foreigner is not a person in this country’: xenophobia and the informal sector in South Africa’s secondary cities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it