Bibliographic record
Abstract
Payment system is an integral part of the entire banking system in every country. Payment systems in centrally-planned economies differ greatly from market-driven economies. Almost all market-driven economies depend heavily on latest technology for efficient functioning of payment systems. The same technology is also a source of risks, which are found only in technology oriented payment systems, such as systemic risk. The discussion on LVPS assumes important dimensions due to its direct implications on financial market. The efficient functioning of payment system is necessary for the efficient functioning of the financial sector. Strong and sound payment system is required not only for long term stability of financial system but also for trouble free day-to-day working of settlements. The transaction on the financial market, generate risks for counterparties who undertake them, for the bankers, for the other intermediaries and for the central bank. The risks are greater in the case of LVPS. The disturbances in these settlements can have wider repercussions for the financial system and the economy as a whole. Due to application of technology the time taken for settling the transactions has been drastically reduced increasing to large volumes exposures. The structure of payment system determines the type of risk, who bears the risks and the vulnerability of the system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".