Mechanism of Functioning of Agriculture: Classification Aspect of Modern Research for the Purpose of Improvement
Bibliographic record
Abstract
The problem of providing the population of Russia with food is being addressed by all economic entities concerned with its solution, including the owners and managers of enterprises – representatives of ministries and departments, academic economists. They see the solution of this problem in the establishment of an adequate mechanism of functioning of agrarian production branches, which would influence these branches and thus increase their effectiveness. However, the existing developments and the utilized mechanism are not perfect and do not reflect the ability to significantly improve the situation in the field of agriculture. In this regard, a task of improving the existing mechanism by focusing its action on the radical improvement of the situation in agriculture remains relevant. The improvement process is not simple. It implies a coherent implementation of the following stages: study of the existing developments of the mechanism of agriculture for their presence; characteristics of the features of the selected criteria and the internal structure of the mechanism elements; classification of these developments according to research purposes and elements included in the mechanism; identification of the shortcomings of the created models using the evaluation of the rational correlation of internal components; justification of the choice of the areas of improvements based on the establishment of classification groups of the mechanism models. This publication implements the named steps that aim to improve the mechanism of functioning of agriculture.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".