Identification of sources of formation of the State Budget of Ukraine and directions of use of financial resources in the context of the russian-Ukrainian war
Bibliographic record
Abstract
The article analyzes the sources of financial revenues to the State Budget of Ukraine and the use of financial resources in the context of the russian-Ukrainian war. The changes in the structure of revenues were investigated. It is established that tax and non-tax revenues have significantly decreased, and the main sources of funding have become international loans and grants, non-repayable aid, and funds raised through the issuance of military bonds. Taking into account these changes in the sources of state budget revenues, international financial assistance plays a crucial role. The United States, the European Union, Japan, Canada, the United Kingdom, and other countries provide Ukraine with significant financial resources that are used to finance defense, social benefits, infrastructure restoration, and other priority areas. The largest share of contributions comes from Anglo-Saxon countries, with the United States providing the most financial assistance, and the European community including the EU's collective institutions, the European Commission and the Council. Among other donors, Japan, the World Bank Group, and the IMF are leading the way. Humanitarian aid is provided by EU member states, the United States, and Japan. About 42 countries have become donors to Ukraine. In terms of military assistance, Ukraine's largest partners are EU member states, EU collective institutions and the European Peace Fund, the United States, the United Kingdom, and Norway. The government finances security and defense sector expenditures exclusively through its own tax revenues and military bonds. The most significant increase in expenditures is observed in the following items: defense, public order and security, social protection and social security, general government functions, health care, and intergovernmental transfers.
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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".