Cognitive Modeling of Sustainability of the Russian Financial Market
Bibliographic record
Abstract
Object: The stability of the financial market is one of the most important components of the inflow of capital into the country and ensuring economic growth. Cognitive modeling of stability of the Russian financial market is carried out.Purposes: Drawing up a cognitive map of the Russian financial market, impulse modeling of changes in its segments in order to find the main factors of stability of the national financial market.Methodology: Cognitive research methods: cognitive analysis and cognitive modeling.Result of research: The stability of the financial market is formed due to the cumulative effect of all its segments. However, the Russian financial market is most sensitive to changes in the money market, foreign exchange market, corporate and government borrowing market. Despite the sanction’s restrictions, the domestic market remains dependent on international financial markets.Application: The results are applicable in the formation of financial and monetary policy of the country.Summary: Achieving stability in the financial market requires constant attention from the regulator for liquidity in the market, stability and predictability of the national currency. The priority direction of development of the state financial policy in the near future should be the establishment of relations with leading players in the world financial markets and international financial institutions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".