WORLD EXPERIENCE IN ADMINISTRATIVE AND LEGAL SUPPORT OF FOOD SECURITY
Bibliographic record
Abstract
The purpose of the study is to improve the legal support of food security in Ukraine, taking into account foreign best practices in this area. The subject of the research is a comparative analysis of the administrative and legal support of food security in the USA, Canada, Japan and the EU countries, according to the areas of state support and development of the agricultural sector, state social policy and social support of the population, and state regulation of product quality assurance. The generalisation of trends in foreign experience can be used to improve the administrative and legal regulation of the food security system of Ukraine. The methodological background of the study is a combination of theoretical and general scientific methods: analysis, synthesis and comparison, theoretical and logical generalisation. The study systematises the strategic directions of state regulation of food security in foreign countries (in particular, the United States, Canada, Japan, and the European Union) in accordance with the directions of state regulation of the agricultural sector, state social policy and social support of the population, and state regulation of product quality. Each country has a unique approach to food security, depending on domestic policies, resources and global market conditions. However, all the countries analysed share the common goal of ensuring safe, high-quality and affordable food for their populations, while developing a sustainable agricultural sector. This study analyses the food security challenges and threats facing Ukraine during the Russian-Ukrainian war (2022-2024). These factors contributed to the adoption of administrative and legal strategic decisions aimed at improving food security and economic sustainability of the country, namely: the law that restored the work of the State Land Cadastre, joining the signing of the Roadmap for Global Food Security; the Food Systems Transformation Plan for the period up to 2030; the Resolution "On Ensuring Stable Operation of Food Producers under Martial Law"; the National Food Security Platform; the draft National Target Programme for Land Use and Protection until 2033 and other regulatory documents aimed at stabilising the agricultural sector, restoring land use and improving the country's food security in times of war. Conclusion. Based on the research, Ukraine has a unique opportunity to improve the administrative and legal support of food security, using the foreign experience of progressive countries with a high level of the Global Food Security Index. At the same time, Ukraine is experiencing a crisis related to the consequences of the Russian-Ukrainian war, in particular: deterioration of food security; reduction of the population's purchasing power due to higher prices for basic foodstuffs; reduction of the amount of sown areas in the agricultural sector; reduction of the country's capacity. The government has adopted a number of laws, regulations, acts and measures to improve food security and economic sustainability. The implementation of these measures is aimed at achieving the following goals: replenishing the agricultural market by supporting producers; restoring food industry enterprises; ensuring the availability of food for all segments of the Ukrainian population; strengthening food security by replenishing state reserves with food; creating a food security monitoring system; prioritising the de-mining of agricultural land; adapting to climate change, etc.
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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 | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".