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Record W4285136565 · doi:10.32843/infrastruct66-5

THE ANALYSIS OF THE MAIN INDICATORS OF INTERNATIONAL TECHNICAL ASSISTANCE OF UKRAINE

2022· article· en· W4285136565 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueMarket Infrastructure · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)EnforcementEconomic growthDemocracyBusinessEconomic policyEconomicsPolitical sciencePolitics

Abstract

fetched live from OpenAlex

The implementation of Ukraine's social and economic development program requires significant funds. Domestic sources of funding are very limited and improve the economy to support vital areas of society. Therefore, the Government of Finance of Ukraine pays special attention to finding sources of economic development. One such source of funding is international technical assistance, the main task of which is to promote economic and social transformation in countries with economies in transition, including Ukraine. The purpose of the article is to analyze the main indicators of international technical assistance to Ukraine. The article is devoted to the analysis of the main indicators of international technical assistance to Ukraine. In the article, the authors analyze and provide the number of international technical assistance projects by development partners. The article presents statistics on the number of international technical assistance projects by sector, namely law enforcement reform, energy efficiency, agriculture and land market development, education and science reform, judiciary, health care and reform, ecology and household waste management, development democracy and the electoral process, deregulation and development of entrepreneurship, development of trade and export potential. In the article, the authors examined the amount of international technical assistance provided by sector in accordance with the estimated cost of projects. In the article, the authors consider the amount of funds used for international technical assistance in the framework of existing projects in 2020 by development partners, namely from the European Union, Great Britain, the United States, Germany, the Organization for Security and Cooperation in Europe, the United Nations , Canada, Nordic Environmental Finance Corporation, Switzerland, Norway, Sweden, European Investment Bank, International Bank for Reconstruction and Development, Japan, Denmark. The article presents statistical data on the amount of international technical assistance provided by forms of assistance, namely the purchase of vehicles, equipment, communication services, rent, software, business trips, conferences and media events, trainings, construction work, repairs, information services and consultations.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.204
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.005
GPT teacher head0.202
Teacher spread0.197 · 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