Bibliographic record
Abstract
In connection with the full-scale armed aggression of the russian federation against Ukraine, the decree of the President of Ukraine "On the introduction of martial law in Ukraine" dated 24.02.2022No. 64/2022 introduced martial law in Ukraine, which is currently extended.From the standpoint of debt policy, martial law is always a crisis-generating challenge for the public finance system of any country, and Ukraine is no exception.The conflict, armed aggression, and socio-economic transformations fundamentally transformed the country's debt policy, requiring balanced strategic decisions to ensure financial and economic stability, financial security, and the ability of public finances to cover immediate and strategic needs in war and post-war reconstruction.Thus, since the introduction of martial law, the soviet-centric methods of conducting hostilities on the part of the russian federation have caused an exponential increase in spending on security and defense forces, social needs, primarily of a humanitarian nature, and limited restoration ("patching holes") of critical infrastructure.The exponential growth of expenses against the backdrop of economic stagnation brought the total state and state-guaranteed debt of Ukraine from February 28, 2022 to May 31, 2023 in hryvnia equivalent to a level of more than 68% or up to UAH 4,594 billion, and the ratio of state debt to GDP received a historical maximum -more than 80% [1].In the structure of the state debt (excluding the guaranteed one), the share of domestic debt, which is primarily represented by obligations to OVDP (Domestic state loan bonds), is about 34%, external debt is about 66% [2].Most of them (54% [1-2]) are formed from borrowings from international financial organizations such as the International Monetary Fund, the European Bank for Reconstruction and Development, the European Investment Bank, etc.About 30% [1-2] are debts of Ukraine under Eurobonds that have been issued over the past ten years, the remaining 16% [1-2] are financing allocated by Great Britain, Italy, Canada, Germany, a few designated foreign agricultural (Cargill) and financial and credit companies (Deutsche Bank) to cover immediate budgetary needs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".