USE OF POSITIVE EXPERIENCE OF DEVELOPED COUNTRIES IN REGULATION OF AGRICULTURAL PRODUCT MARKETS IN UKRAINE
Bibliographic record
Abstract
The scientific article provides a detailed description and analysis of the experience of developed countries in regulating agro-food markets. Much attention is paid to the experience of the European Union countries. The main directions of state support of food and agricultural production within the framework of the Common Agrarian Policy of the EU are determined, special attention is paid to the models of state support of rural development of the EU countries. Emphasis is placed on the three-component structure of rural development policy in the EU, namely: support for agricultural producers, environmental protection, support for comprehensive rural infrastructure development projects. The ability of EU countries to actively stimulate national agriculture, in which the cost of production per unit of output is usually higher than the world. Mechanisms of state regulation of the agro-food system in the USA, Canada and Japan are considered. The analysis of the policy of financing social food assistance to the population in the USA is carried out, the key strategic tasks are assigned to the Service for Food and Consumer Services, in particular, certain state programs of food assistance to the population in the USA are described. As a result of researches for Ukraine the practice of foreign experience of concrete countries of the world concerning the state support of development of agro-food markets and agricultural production is offered, namely: 1) purchase of surplus agricultural and food products from farmers at the expense of state budget funds to maintain purchase prices and guarantee the profitability of agricultural producers (USA experience); 2) the creation of a state institution to stimulate the export of state products, including food and agro-industrial (the experience of the USA, Japan, Germany, etc.); 3) introduction of a system of stimulating the export of Ukrainian food products through commodity lending under the guarantee of export credit agencies, which will allow importers to raise funds for a long time (the experience of the USA, Japan, Poland, Germany); 4) introduction of a system of “land outsourcing” – the purchase or lease by food-importing countries of agricultural land abroad (the experience of China, India, Saudi Arabia). Keywords: agrobusiness, agro-food products, experience of EU countries, common agricultural policy, support of agricultural producers, state food aid programs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".