CLASSIFICATION OF PAYMENT SYSTEMS IN ELECTRONIC COMMERCE
Bibliographic record
Abstract
The article examines the payment systems used in the world and in Ukraine, their features and components, existing varieties. It has been proven that payment systems based on electronic money include: payment systems based on smart cards; softwarebased payment systems operating on the Internet; payment systems based on mobile communication networks. The existing classifications of payment systems are given according to various characteristics: by the method of execution, by the type of operation, by the form of ownership, by the territory of operation, by the degree of residency of the participants, by the volume of payments made, etc. The article, based on its own long-term experience of working with various electronic payment systems, offers its own classification of payment systems by the scope of coverage: a) global payment systems; b) local (national) payment systems. The main global payment systems of the world and local payment systems of the United States, Canada, Great Britain, the countries of the European Union and Ukraine are indicated. A general scheme of the entire system of electronic payments has been built. The mechanism of making payments through LiqPay, WISE and PAYONEER payment services and the features of the functioning of these payment systems are described. The nuances of the acquiring payment transaction processing process, as well as the procedure for introducing an international bank account number (IBAN) in Ukraine, were traced and indicated. Facilitation of greater integration of the Ukrainian payment space with the European one due to savings on bank commissions and speed of operations was revealed. The dependence of the development of electronic commerce on the perfection, volume and convenience of payment systems is singled out.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".