Bibliographic record
Abstract
Fintech is today not only a hot mass media discussion of the future of the financial sector, but also real projects that change banking and financial services. The paper describes features and characteristics of contemporary Russian fintech landscape and ecosystem. The examples of innovative financial services in Russia, including online banking and accounting, new payments and transfers services, platforms for crowdfunding and peer-to-peer lending, blockchain initiatives, etc. are discussed. It is shown that fintech initiatives have not yet led to a radical transformation of the financial sector in Russia because participants of the fintech ecosystem have different points of view on fintech. Russian banks are now developing fintech initiatives within themselves, encouraging technology companies and fintech startups to focus their efforts on innovations that are aimed at improving processes, rather than opening new markets. The Government directs the main efforts to initiatives related to regulation of cryptocurrencies circulation and to introduction of blockchain in regtech and cybersecurity. Customers are interested in new and more convenient functionality in mobile applications, and they are waiting for new value propositions, including fast international money transfers, roboadvising, personal financial management, peer-to-peer lending.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".