The Digitalization of the Russian Financial Market: The Use of Technologies of the Distributed Ledger by the Institutions of Custodian Infrastructure
Bibliographic record
Abstract
Over the past 30 years financial markets have moved from a highly centralized to a globalized system that includes all the world's stock exchanges and other financial institutions. The global stock architecture has united the world market. Traditionally, trading in financial instruments took place between stock brokers and traders who made personal transactions on trading platforms. At that time, stock brokers were monopolists of the market, only their information, their recommendations were the only source of information for investors. This was before the dotcom revolution, when communication became simple and accessible to everyone. Trade has been transformed from physical to electronic form, today you can buy securities, currencies and any derivatives in any quantity, at any time and from anywhere in the world. Development of blockchain technologies is integrated into financial transactions. Financial intermediaries are forced to follow the market and actively introduce new technologies in their processes. This article will consider the possibility of using the technology of the distributed ledger by institutions of custodian infrastructure. Today, a number of Russian financial institutions are developing their own projects using blockchain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".