Racial Desegregation and Schooling in South Africa: Contested Geographies of Class Formation
Bibliographic record
Abstract
Much research on racial desegregation in South Africa uses residential data to track how richer black South Africans are moving from apartheid spaces to higher income suburbs; how racial privilege is giving way to class privilege. Drawing on geographers' relational conception of space and anthropologist Sherry Ortner's notion of a ‘class project’, in this paper I show the importance of geographies of schooling to class formation. The study tracks how schools and two groups—township residents and poorer shack residents—affect and navigate access to schools in Durban. Of importance to class formation, the study finds that children of relatively poor, but not the poorest, township dwellers can commute very long distances to attend prestigious schools. Consequently, racial mixing is more evident in South Africa's schools than in its residential areas—the opposite scenario to that found in many other countries. Yet children born to very poor residents of urban informal settlements face considerable barriers when trying to access well-resourced schools: although they are legally entitled to attend prestigious schools located close to informal settlements, they can often live with extended families hundreds of miles away in rural areas. This new geography of schooling leads to the marginalization of some children but the perception of, and potential for, intergenerational class mobility among a quite significant group of black South Africans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".