Can a Racist Society Produce and Sustain Just and Healthy Interracial Relationships? A Few South African Case Studies
Bibliographic record
Abstract
In this study the experiences, perceptions and challenges of being in a mixed-race relationship (M-R) are explored against the backdrop of previous South African pieces of legislation meant to keep the various race groups apart. The study is located within a conceptual framework predominantly informed by a constructivist approach, including some tenets from the social constructionist approach. For the purposes of this study, six cases of mixed-race couples consisting of black and white partners only were recruited through snowball sampling. The results of the study indicate that individuals found their involvement in M-R relationships to be a positive experience, and thus resulting in a positive attitude change and a sense of personal growth. However, M-R couples and their extended families experienced cognitive dissonance which required them to discard their previously internalised racial stereotypes. To do this, strategies such as cognitive differentiation, re-categorization and de-categorization were used. This enabled the couples and their families to attempt the shift toward non-racial socially constructed categories. Most of the challenges of being in M-R relationships were experienced on both the interpersonal and the inter-group levels. The losses, disadvantages, challenges, concerns and pains experienced by M-R couples were mainly related to family and social disapproval as well as general family and social efforts aimed at discouraging race mixing.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".