Banking on refugees: Racialized expropriation in the fintech era
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Fintech and digital financial services involve the delivery of financial products and services through technology. Fintech companies are part of a financial lending infrastructure claiming to offer an alternative to ‘big banks’, and are often touted as digitally disruptive technology that is rapidly reshaping financial inclusion agendas and improving the lives of the poor. For many refugees living in camps and informal settlements in Kenya, fintech is often the only viable option for credit or microfinance aid. While refugees are often excluded from credit, the spread of fintech as a solution for direct peer-to-peer aid transfers from the Global North to refugees has resulted in the uneven distribution of credit access and livelihood support. Through fintech, private citizens and groups in the Global North are able to disrupt and subvert refugee assistance, deeming some worthy of aid while others face ongoing exclusion. While fintech remains a hopeful source of greater efficiency and empowerment, the direct transfer of aid money masks profit and corporate power by only extending assistance to those refugees who are appropriately entrepreneurial, that is to say those who will start small businesses and pay back their loans. This paper argues that processes of financial inclusion carried out by and through fintech are still distinguished largely by exclusion. In so doing, this paper highlights a theoretical position that refugee governance is embedded in racial forms of capital accumulation and expropriation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it