Impact of Kazakhstan’s Integration into the Eurasian Economic Community on the Competitiveness of the Country’s Agriculture
Bibliographic record
Abstract
The aim of the article is to identify the impact of Kazakhstan’s integration into the Eurasian Economic Community (EAEC) on the competitiveness of the country’s agriculture. To achieve target aim scientific works of foreign scholars on the problems of integration and its impact on the national economy have been analyzed. The study found that regional integration has positive and negative effects that can lead to further progressive development of the country and its industries, but also exacerbate existing conflicts and crises. To evaluate the adaptability of Agriculture of Kazakhstan to the country’s membership in the EAEC, indicators of industry competitiveness were analyzed: crop yields, livestock productivity, profitability, amount of state support, index of net exports, production of main agricultural products per capita. It was revealed that in agriculture of Kazakhstan competitiveness is lower than in Russia and Belarus in many positions. The world economy has positive experience of management and development of agriculture in terms of integration. Good example is the Common Agricultural Policy (CAP), conducted in the European Union, thanks to which Europe hasn’t simply provided itself with all the necessary food, but has become a major supplier of agricultural products to the world market. The article proves the positions of the CAP, which are recommended for use within the EAEC. Currently, in order to support agriculture in Kazakhstan it is necessary to create incentives for the consolidation of small farms, contribute to increase in incomes and salaries for farmers and agricultural workers, increase the amount of state support, significantly expand the range of agricultural products, purchased by the state.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".