Foreign experience of innovative development of the agro-industrial complex.
Bibliographic record
Abstract
The relevance of the topic of the article - the issues of innovative development of agroindustrial complex of Kazakhstan, which provides large-scale production, require detailed study. The goal - is to consider the positive aspects of the best practices of foreign countries regarding this problem and develop practical recommendations. Methods - generalization, quantitative and qualitative analysis, abstract logical. Results - in order to apply high technologies in agricultural sector, economically developed countries use subsidy systems, price support (USA), government assistance in obtaining income per hectare and payments for livestock (EU countries), income support through payments (Canada) and concessional lending (Brazil). In addition to financial assistance, agricultural producers in the USA, Canada and other countries are provided with information, legal, innovation, marketing, insurance and other types of support. Conclusions - innovative processes, expansion of the competitive environment in the AIC presuppose the effective use of scientific and technical potential, integration of science, education and production, technological modernization of the economy based on progressive methods. Innovation is reflected in the implementation of the strategic objectives of ensuring food security and effective regulation of the domestic food market in order to stimulate labor productivity in agriculture, increase export potential of agricultural sector. It is necessary to combine innovative activity in domestic agro-industrial production with international practice, which will increase production capacity of agrarian sector of the republic. The development trends of the world market convincingly show that there can be no other way in Kazakhstan than the formation of a new type of economy, widespread innovation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".