Monetary Measures of Macroeconomic Regulation of the National Economy
Bibliographic record
Abstract
Monetary policy occupies one of the key positions in macroeconomic regulation, on the effective implementation of which the stability of economic growth, the reduction of unemployment to natural levels etc.The purpose of the study is to identify the results of the practical application of the monetary theory's theoretical generalisations for the use of effective monetary policy instruments in Ukraine under conditions of socio-economic instability and weak inclusive institutions of national economy development.The study systematises the main vectors of monetary policy improvement and possibilities of its adjustment.By analysing the key aspects of the institutional framework for monetary regulation under conditions of financial instability, it is established that, in coordinating fiscal and monetary policy, the independence of the central bank should be the primary condition for the construction of an optimal macroeconomic policy.The results of the study underline: price stability should be a prerequisite for the resumption of lending and economic activity after the recession caused by the cyclical crisis exacerbated by the COVID-19 pandemic.The results obtained provide insight into the current state and prospects of developing monetary policy in Ukraine, which in turn should be oriented not towards matching the exchange rate parameters with the volume of domestic government bonds issue (DGBs), but towards ensuring strict fiscal measures aimed at overcoming the effects of the budget crisis and achieving macroeconomic balance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".