Geographies of Fintech and Everyday Life: Reconfiguring Spaces, Practices, and Scales of Digital Money and Finance
Bibliographic record
Abstract
ABSTRACT Financial and monetary technologies, understood both through their digital platforms and the materiality of mobile devices, are increasingly pervasive, and now shape the economic practices of individuals, households, and communities. In this critical synthesis we bring together research in economic geography on everyday life and financial and monetary technologies (fintech) to show the centrality of these technologies to daily life, and to suggest ways that research on the geographies of fintech could be deepened through attention to everyday life. We show how a focus on everyday life highlights the impact on fintech end‐users, such as consumers and debtors, and their relationship to the (re)privatization of social reproduction, across a range of practices, spaces, and actors. We also show how financial technologies and digital platforms contribute to the construction and production of the mundane and habitual routines of daily life and connect up often‐thought distinct sites and scales of activity. To demonstrate the value of our critical synthesis, we then analyze two examples, “Buy Now, Pay Later” (BNPL) and “Earned Wage Access” (EWA). These examples show how fintech products reconfigure the everyday life of payments, wages, and debt management, especially for cash‐constrained consumers from whom these products are specifically designed to profit. Critically, we build the literature on everyday life and fintech in economic geography to argue that a synthesis of these literatures—the everyday life of fintech—provides a fruitful avenue to pursue new research at the intersection of the geographies of money and finance, platforms, and the digital economy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.030 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".