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INTERNATIONAL FINANCIAL SUPPORT AS PART OF THE BUDGET OF UKRAINE DURING THE WAR

2023· article· en· W4392568812 on OpenAlexaboutno aff
Т.І. Батракова, E.S. SEMIBRATOVA

Bibliographic record

VenueEconomic innovations · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceFinanceBusinessFinancial system

Abstract

fetched live from OpenAlex

Topicality. russian aggression against Ukraine has caused a serious crisis in our country. A full-scale military invasion dealt a significant blow to the economic, social, humanitarian systems of Ukraine. In all spheres of life of the country there have been negative changes, including in the financial. Ukrainian GDP fell by 29% last year. Budget revenues decreased significantly, and expenses increased several times. This process led to a significant deficit of the state budget. To solve this problem, additional financial resources are needed. The state attracts them from international partners in the form of grants and loans. Aim and tasks. Constant monitoring of the structure of international financial support, especially during the russian aggression, has a very important purpose due to its significant impact on the social and economic processes taking place in the country. The article is aimed at analyzing the financial assistance that Ukraine has received since the beginning of a full-scale war. The main objectives are to consider the dynamics of loans and grants, as well as their impact on the future financial development of the country. Materials and Methods. Particular attention is paid to international partners who form a large package of financial assistance for our country, consisting of support for the state budget and ensuring financial stability, the formation of funds for the restoration of the country and the economy, as well as financial support for humanitarian issues. It is analyzed from which countries and international organizations funding was provided. The conditions and ways of financial support of Ukraine are considered. Research results. As modern economic policy shows, Ukraine received a significant share of funding in the form of grants and loans from the USA, Canada, Germany, Great Britain. The European Union, the World Bank, and the International Monetary Fund also play an important role in providing financial assistance. In the future, irrevocable funds should come from sources associated with the aggressor. russia must pay with its own funds for the unleashed war in our country. Conclusion. Financing the urgent needs of Ukraine is now an urgent issue. It is the external financial assistance that supports the functioning of the state sector of the Ukrainian economy and helps to balance the monetary and financial position of the country. This assistance is important to support the economy of Ukraine, the exchange rate of the national currency and protect the territorial integrity of our state.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.001

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 head0.021
GPT teacher head0.237
Teacher spread0.216 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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