ЗЕРНОВАЯ ПОЛИТИКА ЗАРУБЕЖНЫХ СТРАН ШЕТ ЕЛДЕРДІҢ АСТЫҚ САЯСАТЫ
Bibliographic record
Abstract
The article is devoted to the organization and regulation of agricultural raw materials and food markets, which components include the control over the provision of budget subsidies (the national aspect), and pan-European measures aimed at ensuring the producers incomes, maintaining retail prices at the optimum level. The agricultural policy of the EU countries with significant differences in the directions and methods of financing agro-industrial complex is considered. Foreign countries use half of the national agricultural budgets to finance structural policies: modernizing and enlarging farms, increasing soil fertility and other agricultural resources, creating conditions for effective farmers, reduced production costs. The research results showed that in the main grain- producing countries, grain-production is subsidized. The EU countries, the USA, Canada, Japan, and India spend significant financial capital on the improvement of grain production technologies. In the US and the EU, the priority direction for using funds to support the services sector is product promotion to the markets. Assistance to agricultural producers in Kazakhstan, taking into account foreign experience of State regulation with full consideration of the characteristics of market relations and economic situation, is of particular importance and relevance in terms of the republic’s accession to the WTO and toughening the competition for the domestic grain and bakery products in the world 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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.007 |
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".