Monetization of the Economies as a Priority of the New Monetary Policy in the Face of Economic Sanctions
Bibliographic record
Abstract
The purpose of this paper is to conduct a comparative analysis of monetization as a priority of the new monetary growth of the economies using the example of the Russian economy, identifying new trends in global practices of monetary factor management, as well as the search for ways to stimulate economic growth using the best international experience. Our paper tackles the novel research question of whether changing the priorities of monetary policy from targeting (and curbing) inflation to stimulating economic growth might yield more favorable economic results and what best world practices should be appropriately introduced in Russia to improve the effectiveness of monetary policy. The key results of the paper are focused on a comparative analysis of the economies’ development under the influence of monetary factors in comparison with the most progressive economies, the study of the best practices for increasing the monetization of national economies, and the identification of recommendations for determining the most optimal way to increase economic growth through the monetization of the economy. Monetarist views on the decisive role of fiat money in the development of the real sector of the economy, capital markets, payment and settlement systems, the standard of living of the population, and other important aspects of macro- and microeconomics have become the mainstream of government regulation. It seemed that by finding the right indicators of the relationship between interest rates, GDP, and inflation, all problems of economic growth could be solved. By increasing the amount of money faster than the achieved economic growth, it was believed that it was possible to stimulate GDP growth through monetary investments and credit, i.e., more money was issued than the value produced represented by the goods and services. Accordingly, new money that had no value had to create new value. We argue that monetization can be seen as the main factor in providing such incentives. Our results can be useful for central bankers, policymakers, and stakeholders in the banking and financial sector. The conclusions and recommendations of the authors are based on studies conducted using such research methods as content analysis, logical analysis, and statistical analysis.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".