‘If I speak English, does it make me less black anyway?’‘Race’ and English in South African desegregated schools
Bibliographic record
Abstract
This article focuses on the role language plays in constructing youth identities that are in flux in desegregated suburban schools in South Africa. Interview and participant observation data were collected in three racially mixed schools in Johannesburg. My analysis of the data is set against a discussion of the problematic concept of race and of the historical classification of South African English according to ‘race’ as well as the position of English in South Africa at present. The article presents an analysis of the ways in which learners recognize and characterize the different kinds of English used around them, attaching prestige to varieties perceived as white. The tension between learners' valuing of what is perceived as white English and their labelling of black learners who ‘speak like a white person’ or who no longer speak African languages (either through lack of proficiency or choice) as ‘coconuts’ is explored. The article attempts to open up a debate on race and language use among youth in South Africa, and on race and varieties of English in particular.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".