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Міжнародний досвід регулювання державного боргу та можливість його адаптації до українських умов

2025· article· uk· W4416558557 on OpenAlexaboutno aff
Maksym Urakin

Bibliographic record

VenueВісник Академії праці, соціальних відносин і туризму. Серія: економіка, психологія та управління. · 2025
Typearticle
Languageuk
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsDebtDebt restructuringRestructuringInternal debtTransparency (behavior)External debtDebt levels and flowsBudgetary policyFlexibility (engineering)Debt-to-GDP ratio

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0070.010
Meta-epidemiology (broad)0.0110.005
Bibliometrics0.0070.008
Science and technology studies0.0040.004
Scholarly communication0.0040.004
Open science0.0110.006
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0230.040

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.

Opus teacher head0.018
GPT teacher head0.231
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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