Inter-racial Marriages in South Africa
Bibliographic record
Abstract
Using the ten percent sample of the 1996 South African census, we examine the rates of intergroup marriage and marriage between linguistic groups in South Africa. Since whites are a small number in South Africa but historically have held most of the power, the analysis provides an interesting context to test the generalizability of theories about inter-racial marriage. We test exchange theory by examining the effects of education on the patterns of intergroup marriage. We do this while controlling for relative group size. Finally, we examine the socioeconomic status of children of mixed marriages to see possible implications of mixed marriages for future generations. Although education is only weakly related to rates of inter-group marriage, it appears to facilitate outmarriage for low-status groups. More minority females than males marry out of their own group, a pattern of intermarriage quite different from that of the United States. This pattern may reflect local norms, or the different racial composition of the two countries. Children of mixed-white marriages appear to do much better economically than children of mixed-black marriages.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".