Social sorting as ‘social transformation’: Credit scoring and the reproduction of populations as risks in South Africa
Bibliographic record
Abstract
Abstract In this article, I show that credit scoring, although not explicitly designed as a security device, enacts (in)security in South Africa. By paying attention to a brief history of state-implemented social categories, we see how the dawn of political democracy in 1994 marked an embrace of – not opposition to – their inheritance by the African National Congress. The argument is placed within a theoretical framework that dovetails David Lyon’s popularization of ‘social sorting’ with an extension of Harold Wolpe’s understanding of apartheid and capitalism. This bridging between Lyon and Wolpe is developed to advance the view that apartheid is a social condition whose historical social categories of rule have been reproduced since 1994 in the framing of credit legislation, policy and scoring. These categories are framed in the ‘new’ South Africa as indicators of ‘social transformation’. Through the lens of credit scoring, in particular, it is demonstrated that ‘social transformation’ not only influences, shapes and reproduces historical forms of social categories, but also serves the state’s attempt to create and maintain populations as risks.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".