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Record W4200309828 · doi:10.33763/finukr2021.11.045

International economy: deepening and expanding research potential in Ukraine

2021· article· en· W4200309828 on OpenAlexaboutno aff
Yevhen SAVELIEV, Vitalina Kurylyak

Bibliographic record

VenueFìnansi Ukraïni · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Issues in Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsUkrainianCompetition (biology)Context (archaeology)Government (linguistics)State (computer science)Political scienceStaffingInternational tradeBusiness

Abstract

fetched live from OpenAlex

The topical issues of the development of the research potential of Ukraine in the field of international economics , capable of creating scientific support for the foreign economic activity of entrepreneurial structures and government organizations in the context of world and European integration, have been investigated. The creation of the infrastructure of research organizations specializing in the international economics has been substantiated, in particular, the feasibility of creating research institutes in the USA and Canada, Europe, and the Center for International Agricultural Business. The article considers the expediency of conducting research on the issues of cooperation with interstate integration associations of countries, including the EU, ASEAN, TPP, APEC, BRICS, for the implementation of the country's foreign economic policy. A special place in the system of international economics research should be occupied by the problems of Industry 4.0 and the leadership of Ukrainian IT companies in the system of global economic competition. The state of the staffing of research activities in international economics is analyzed and proposals for training of highly qualified specialists in international economics in large industrial centers: Kharkov, L’viv. Dnieper, Odessa and Zaporizhia are formulated.

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)0.001
metaresearch head score (Gemma)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.316
Teacher spread0.243 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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
Published2021
Admission routes1
Has abstractyes

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