Bibliographic record
Abstract
In the conditions of a full-scale war between Russia and Ukraine, our state faces the question of attracting funds from both residents and non-residents to solve military and social problems, as a result of which the national debt is formed and its constant growth occurs. The receipt of macro-financial assistance (soft loans) from the EU had the greatest impact on the growth of public debt. The growth of Ukraine’s public debt was influenced by the receipt of loans under the programs of the IMF, the World Bank, the EBRD, and the government of Canada. The growth of the national debt determines the need to choose an effective method of managing the national debt, because the country’s debt security depends on the management of the national debt. The article analyzes the management of public debt from a theoretical and scientific point of view. The purpose of the work is to conduct an analysis of the economic category of «public debt management», its principles and stages. The main areas of activity of the concept of «public debt» are determined both from a regulatory and legal point of view, and from a scientific point of view; its classification by criteria is given. Public debt is an important economic category that reflects a set of economic relations in the field of attracting positional financial resources with the involvement of the financial deficit of the national sector of the economy on the basis of repayment, payment and maturity.
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 | 0.001 | 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) | 0.003 | 0.006 |
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; both teacher heads agree on what is shown here.
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".