From theory to reality: The varied and often unintended consequences of card payment surcharging
Bibliographic record
Abstract
This paper examines the practice of surcharging card payments, which is designed to increase transparency and reduce the cost of card acceptance for merchants. However, the theory behind surcharging does not always align with its outcomes in practice. Despite its intended benefits, such as encouraging cost-effective payment methods and reducing merchant service fees, the global implementation of surcharging has produced mixed results. While surcharging is legal in certain markets, like Australia and the USA, it often leads to overcharging by small merchants, a lack of consumer benefits, and regulatory challenges. This paper explores the current state of surcharging across key markets, identifies the main beneficiaries of the practice, and highlights the need for stronger regulation of payment acceptance costs. Ultimately, it argues that surcharging has failed to meet its goals and that a more competitive and regulated payment ecosystem is necessary to achieve genuine transparency and cost reductions.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".