A feminist approach to fintech: exploring ‘buy now, pay later’ technologies and consumer fintech
Bibliographic record
Abstract
Buy now, pay later' (BNPL) is a financial technology that is reshaping online consumption by allowing users to split payment for goods over 3-4 interest-free digital installments.While the use and value of BNPL has risen dramatically, it, and other consumer-oriented fintech, has received relatively little critical attention.Demographically, the majority of BNPL users are young and women and its negative impacts are disproportionately felt by lower-income groups, making this a specifically gendered financial technology.In this paper we develop a feminist approach to studying fintech, which we use to present a critical analysis of BNPL drawing on data from the US, UK, and Canada.Through this lens, we explore BNPL's revenue streams, data collection practices, relative lack of regulation, and how these factors function structurally in the digital payments space, to analyze their impact for consumers and retailers in the context of rising consumer indebtedness and the financialization of consumption.We argue that BNPL is a fintech intervention that attempts to shift consumer practices with distinctly gendered implications for social reproduction, household finance, and everyday relations to debt and money.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.013 | 0.035 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".