The Wireless Wire Do M-Payments and UNCITRAL Model Law on International Credit Transfers Match, Raw?
Bibliographic record
Abstract
In recent years mobile devices have been increasingly used for the transmission of data, including the initiation and receipt of payments. Payments executed include "international remittance transfers" ("IRTs"), which are cross-border, person-to-person payments of a relatively low value. An IRT is likely to involve a "settlement chain" consisting of a series of separate payments. This paper examines the suitability of the UNCITRAL Model Law on International Credit Transfers 1992 ("MLICT") to cover IRTs initiated and/or completed by mobile devices. The MLICT is a comprehensive statute covering rights and obligations incurred in the course of an international credit transfer. The paper concludes with the observation that low-value credit transfers were envisaged as covered by the MLICT and yet were not central in the work leading to the model law. Overall, the ML/CT is appropriate to cover IRTs. Only a few adjustments to the model law should be considered. Consumer-protection aspects, primarily regarding disclosures, should be added; consumers' liability for unauthorized transfers should be rethought and redrafted.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".