Integral assessment of the level of Ukraine’s economic security: Modeling and economic analysis
Bibliographic record
Abstract
The influence of military-political instability at the state security has special consequences and cannot be ignored. That is why the methods of economic security calculations should be adapted to modern conditions. Significant changes in economic, political, social conditions in Ukraine because of the influence of military events cause the necessity of developing approaches to calculations of economic security level. The article aims at analyzing active methods of calculations of economic security level to adapt their indicators to actual economic circumstances in Ukraine and the world. Indicators of the economic security have been composed of budget, money-credit, foreign exchange, debt, known banking financial market security, banking security. Every component of economic security has factors of war influence, which, as a result, correlates with the integral indicator of economic security. The relevance and indispensability of reinforcement of the economy’s competitiveness on security foundations are substantiated. The authors’ methods of calculation of an integral indicator of economic security have been suggested. A taxonomic indicator is used to measure the level of security. Conclusions regarding the preconditions, capacity, and availability of tendencies of economic security in regions of the country have been made. Strategic priorities of the state policy for reinforcement of economic security have been substantiated.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".