Міжнародний досвід регулювання державного боргу та можливість його адаптації до українських умов
Bibliographic record
Abstract
The article explores international practices of public debt regulation and substantiates the possibilities of their adaptation to the conditions of Ukraine’s transitional economy. It is argued that effective public debt management is not only a tool for financing budget deficits but also a critical factor in ensuring macro-financial stability, investor confidence, and national financial security–particularly during wartime and post-war recovery. The study focuses on the analysis of institutional models of debt policy (governmental, agency, and central bank-based), as implemented in developed countries such as the USA, Germany, Japan, Sweden, France, and Canada. It examines key instruments including fiscal rules, debt anchors, public investment management systems, debt restructuring mechanisms, and liability management operations. The findings suggest that in wartime, priority should be given to flexibility and financial survival, whereas in the post-war phase, the emphasis should shift to fiscal discipline, transparency, and strategic planning. The paper proposes an adaptive two-phase model of public debt regulation for Ukraine, based on the principles of duality, institutional quality, and transparency as a national security imperative. It concludes that the effectiveness of debt policy depends not only on the proper choice of instruments but also on the political will to implement deep institutional reforms. Future research should focus on the development of scenario-based models for adapting international practices to Ukraine’s context, as well as empirical assessment of the relationship between institutional quality and the efficiency of debt management in conditions of limited fiscal space.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".