The creation of digital financial infrastructures in Africa and the philanthrocapitalism of the MCF
Bibliographic record
Abstract
Financial infrastructures are socio-technical processes created and sustained by power relations in the global political economy. We examine how the Mastercard Foundation, an understudied development actor, articulates such power relations through its participation in creation of digital financial infrastructures in Africa. Drawing from a social network analysis informed by theories of neocolonialism, we propose a critical engagement with the Foundation's agenda of ‘financial inclusion’. We argue that the Foundation's actions reveal the presence of potential materially extractive relations of power articulated around three markers of neocolonialism: the technocolonial structure of its fundings relationships; the structure of the partnerships enabling data extractivism; and the direction of the relationships thus set in motion. We demonstrate how the digitalization of the payment and credit systems the Foundation supports contributes to the creation of infrastructures of adverse financial inclusion and lays the groundwork for a dynamic of data extractivism that perpetuate forms of neocolonialism.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.000 | 0.006 |
| 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".