Integrated Assessment of Ukraine Monetary Security Level
Bibliographic record
Abstract
In the article, the authors made an integrated assessment of monetary security level by calculating the impact of the monetary system set of indicators and Ukraine's monetary policy.To build an integrated indicator for assessing the level of monetary security, the authors identified 14 main monetary indicators which were divided into stimulants and where stimulants.For each indicator, the authors calculated the level of impact on monetary security.To calculate the integrated level of monetary security assessment, the authors used two methods: with the establishment of weights for each of the output indicators and without it.In this case, the possible values of the integrated indicator of monetary security Wi belong to the segment [0; 1], and its unit and zero values correspond to the best and worst value of the monetary security indicator.With the dignity of the two approaches, calculated indicators of monetary security of Ukraine reached the minimum value in 2015.A similar trend was also characterized regarding to monetary security highest level, observed in 2013.At the beginning of 2020, the level of monetary security was set at 0.558 (0.531).Slight deviations between the calculated integrated indicators of the level of monetary security indicate the accuracy of the calculations of both the first and the second approach.Both approaches can be used in practice by regulators and government agencies.According to the results of the study, we conclude that the integrated indicator of the level of Ukraine's monetary security in 2020 is within satisfactory limits, due to the objective current trends of the national economy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".