The incentives of PayPay against the convenience of cash: On the conveniencing of cashless payments in Japan
Bibliographic record
Abstract
This article offers a two-decade overview of digital payments in Japan, with a focus on the role of convenience stores in the rollout of new payment systems. The aim is both to follow the recent development of code-based payment apps such as SoftBank's PayPay, while also interrogating the appeal to convenience by both the apps themselves and by academic literature on digital payments. The assumption of convenience as a default explanation for the adoption of digital payments in much literature must be resisted in view of Japanese government policy and large-scale incentive campaigns by payment providers encouraging users and retailers to adopt app-based payment systems. In Japan in particular, these incentives were meant to combat the default convenience of cash or other tap-to-pay prepaid cashless options introduced in the early 2000s. The role of convenience stores in the 2018–2019 “cashless payment wars” period under investigation here is to allow the perceived convenience of the retail forms to inflect the experience of the apps themselves. The article further uses this case as an opportunity to reflect on the temporality of convenience itself, as a retrospective explanatory framework for social shifts, and as a process of becoming-default of a new payment system. I term this process conveniencing. The conveniencing of otherwise inconveninent payment apps is the process under analysis here.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".