ORGANIZATIONAL AND ECONOMIC MECHANISM OF LAND RESOURCES USE
Bibliographic record
Abstract
One of the main components of natural resources of the agricultural enterprise is land or land resources. After conducting research and analysis of scientific publications, textbooks and manuals of scientific experts, as well as legislative and regulatory framework, the article found that there is no single definition of the essence of the concept of "land resources". On this basis, scientific approaches to the interpretation of the essence of the category "land resources" were summarized and their clarification was proposed. It was established that land resources can be interpreted as the main natural resource used in various sectors of the economy. The types of agricultural land, which include agricultural land: arable land, hayfields, pastures, fallows, perennial plantations, are substantiated. Also considered the main methodological approaches to assessing the effectiveness of the use of land resources of the enterprise, which allow for a comprehensive analysis, justify the conclusions about the effectiveness of agricultural land. The directions to improve the efficiency of land use by optimizing the structure of crops, the use of crop rotation, the creation of land cooperatives and improvement of the mechanism of land protection are formulated. Since the level of efficiency of agricultural land use will depend on the state of productive forces and the growth of the agrarian sector of Ukraine. Attention is paid to the issue of the concept of "land relations", which are an integral part of the economic and legal functioning of agricultural enterprises. In connection with the completion of land reform in Ukraine, the article defines the features of the regulation of land relations in the leading countries of the world. The experience of the United States of America, Canada, Great Britain, the Netherlands, Denmark, Switzerland and other countries is summarized. The peculiarities of functioning of the model of market turnover of agricultural land are analyzed, which led to the conclusion or the need to introduce a liberalized land market in Ukraine.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".