OPTIMIZATION OF THE MANAGEMENT MECHANISM FOR THE INNOVATIVE DEVELOPMENT OF THE REGION’S AGRICULTURAL SECTOR
Bibliographic record
Abstract
The current trends in the development of innovative activities in Russia are far from fully meeting the expectations associated with improving the competitiveness of products and the quality of life of the population, with the provision of dynamic sustainable growth, and the formation of the innovative economy. The mixed nature of the Russian economy, the fundamentally different technological level and institutional conditions for the development of various sectors exclude the possibility of defining a single model of innovative development that is universal for all sectors. In the current conditions, the technical and technological level of the agrarian sector of the country's economy is the most catastrophically lagging behind the world's leading producers of agricultural products. Domestic agrarian production is 5 times more energy intensive and 4 times more metal consuming, and labor productivity is 8-10 times lower than in the USA, in the leading countries of the European Union and Canada. Not having eliminated this techno-technological backlog, without implementing the advanced development of certain specific areas of scientific research and technological developments in the field of agriculture, Russia's agrarian sector will finally lose its competitiveness and will not be able to ensure the country's food security. Thus, the need for a scientific justification of the theory, methodology and practice of the innovative development management of the agrarian sector of the regional economy in the context of large-scale economic and institutional transformations determines the urgency of the issue. Currently, most of the works of domestic researchers put emphasis on the problems of knowledge transfer, at the same time, the methodology for creating and commercializing competitive scientific knowledge through the formation of innovative agricultural clusters is beyond the scope of scientific research, and its management and economic mechanism has not been developed yet. Thus, the aim of this research is to develop proposals for optimization of the management mechanism for the innovative development of the region's agricultural sector (by the example of the Samara Region of the Russian Federation). The research used a set of methods of scientific knowledge used at both theoretical and empirical levels (conceptual modeling, synthesis and analysis, tabular and graphical interpretation of theoretical information and empirical data).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".