Economic Mechanisms for Managing Food Security in the System “Production-Consumption-Import”
Bibliographic record
Abstract
Green revolution almost spent its resources, and scientists haven’t found the way for the quick increase of potential capabilities of newly bred sorts of grain crops, grain legumes, and cereal crops, of potato, sugar beet, vegetables, and feed crops. Due to impossibility of use of ecological tools, there is a necessity for the search for new ways of providing food security. In this research, economic mechanisms of managing food security in the system “production-consumption-import” are developed. The authors analyze the notion and meaning of economic mechanism, determine the current state of food security of modern Russia in the system “production-consumption-import” in comparison with other countries, and determine problems and perspectives of its increase. As a result of conducted analysis, the authors come to conclusion that current food situation in Russia is characterized with features of chronic lack of food and incapability or lack of wish of their authorities to solve this problem. Socio-economic state of agriculture does not ensure the economic accessibility of food products for all groups of population. As the key economic mechanisms for managing food security, this research offers the formation of growth poles, or economic cores, and creation of agricultural clusters.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".