The rise of microcredit ‘control fraud’ in post-apartheid South Africa: from state-enforced to market-driven exploitation of the black community
Bibliographic record
Abstract
ABSTRACT The end of apartheid in South Africa in the early 1990s did not see the envisaged end to the exploitation of the black South African population, but instead saw simply a shift from state-backed exploitation to market-driven exploitation. This trajectory is especially germane to the country’s microcredit industry, which has spectacularly and wilfully enriched a narrow white male elite while simultaneously helping to fragment and destroy the local rural and urban economies of the black poor. As this article demonstrates, a major aspect of this one-sided enrichment process has involved ‘control fraud’, the process whereby the CEO and senior management of a financial institution use their seniority to defraud customers, shareholders, the government and the general public as they go about maximising their own private short-term financial gains. Already a problem elsewhere in the global South, South Africa has thus joined the growing list of countries that have seen control fraud in the microcredit sector undermine and block progress towards more productive, sustainable and equitable local economies.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".