South African Families of Indian Descent: Transmission of Racial Identity
Bibliographic record
Abstract
This article examines how notions of ‘race’ are constructed and transmitted across generations within South African families of Indian descent. The qualitative data analysed and presented in this paper was collected by means of oral histories and semi-structured interviews with five South African families of Indian descent, and has been drawn from a larger data set that was gathered for my doctoral research. I argue in this paper that the continued focus of the democratic South African state on apartheid-era ‘race’ classification in various legislative and bureaucratic guises has resulted in the embeddedness of ‘race’ thinking in the national psyche. This has set limits to identity choices, and the family, therefore, while being an important site of racial identity formation, is superseded in significance by relationships with varying role players in society, including, and most important in this case, the South African state. The born-free and raised-free generation’s uncritical acceptance of the racial label ‘Indian’ does not conflict with the older generations understanding and appropriation of the category. This further points to the cogency of apartheid legislation in a democratic country, and while agency cannot be denied, this paper argues that agency has to be viewed relative to the constraints imposed by society. This paper demonstrates that rejection of the ‘race’ label by participants or resistance to it, is futile, as legally and socially South Africans of Indian descent are marked as ‘Indian’ by virtue of their phenotype and thus remain trapped by state imposed classifications.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".