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Record W4292133411 · doi:10.22178/pos.83-4

Organisational Aspects of Financial Support for the Agro-Industry: The Experience of Developed Countries

2022· article· en· W4292133411 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

VenuePath of Science · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureState (computer science)BusinessProduction (economics)Developing countryDeveloped countryEconomic policyEconomicsFinanceEconomic growthMacroeconomics

Abstract

fetched live from OpenAlex

The research aims to study the organizational aspects of financial support for the agro-industry of developed countries and to determine the possibilities of their implementation in Ukraine. The article analyzes the experience of state and credit financing of the agro-industry of developed countries (using the example of the United States of America, countries of the European Union, Canada and Japan); their positive features and possibilities of implementation of this experience for Ukraine are highlighted. Studying the experience of financial support of the agro-industry of developed countries, we can conclude that they have developed specific systems for servicing agricultural production. This is caused, first of all, by the peculiarities of this sphere of economy, namely the need to eliminate the phenomenon of economic crises that arise for various reasons: cyclical economic development or socio-economic instability. State support of the foreign agro-industry in multiple countries has positive consequences. However, it is impossible to apply a separate instrument of state support in Ukraine based on foreign experience for various reasons. Firstly, in the EU and other developed countries, the agricultural sector is characterized by the overproduction of farm products. On the other hand, in Ukraine, it is necessary to increase the production of domestic farm products. Secondly, before deciding to increase state support for the agro-industry, it is essential to take into account the level of the budget deficit, for which Ukraine is unlikely to be able to take specific measures compared to other countries. In addition, it is worth taking into account the significant losses of the agro-industrial complex as a result of military operations on the territory of Ukraine and the redistribution of the state budget to minimize these consequences. Without available budget funds, it is impossible to ensure the proper level of development of the agricultural sector of the Ukrainian economy.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.824
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.017
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