Financial inclusion and the contested infrastructures of cash transfer payments in South Africa
Bibliographic record
Abstract
Across much of the South, digital technologies are increasingly central to the expansion of state social protection systems. Supported by major development agencies, many of these distributive technologies are developed and implemented by financial technology companies with the specific aim of accelerating financial inclusion. While researchers have documented the influence of these financial actors and logics over social policy, we know less about how these interventions are transforming the experience of receiving social protection. Based on qualitative and observational research with social grant recipients in South Africa, this research demonstrates how digital and financial technologies produce confusion, informational opacities and new forms of exclusion among grant recipients. It suggests that the increasingly prominent role of financial technologies in the delivery of social protection undermines state capacity and further entrenches the influence of neoliberal logics over social policy. Finally, the article suggests that these technologies may be transforming the nature of social citizenship in South Africa, undermining efforts to advance universal and redistributive social protection policies.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".