Legal principles of agricultural land use in Ukraine and Canada: comparative legal aspect
Bibliographic record
Abstract
The article provides a comparative analysis of the legal basis for the use of agricultural land in Ukraine and Canada. It has been established that the current state of land resources in the majority of Ukraine can be characterized as a crisis, with a tendency towards deterioration, which significantly complicates the socio-economic development of Ukraine. The main reasons for this situation are, first of all, the irrational use of the state’s land resource potential, the deterioration of the quality of the land, the irregularity of the change in the nature of the functioning of the land as a means of production in market conditions, and other negative factors. However, agricultural land, which occupies a special place in the structure of the land fund of Ukraine, needs even greater legal protection. Irrational use of agricultural land is one of the potential threats to Ukraine’s food security. In this regard, there is an urgent need to define an effective management mechanism in the field of land use, including agricultural use. In this context, the issue of land turnover is important in connection with the adoption in March 2020 of the Law of Ukraine «On Amendments to Certain Legislative Acts of Ukraine Regarding the Movement of Agricultural Land». In order to build an effective and competitive land market, it is necessary to have a strong legal basis, and for this it is advisable to turn to foreign experience in this area, especially to the experience of countries with the best functioning land market, such as Canada. The Canadian experience shows the importance of detailed regulation of land use at all levels of government and the need for a coordinated approach to ensure the proper use of agricultural land. The Canadian Constitution gives the provinces the exclusive right to decide virtually all land matters, including those related to the use of agricultural land. The presence of a market for agricultural land in Canada promotes the attraction of additional capital in the form of internal and external investments in the agricultural sector, which in turn stimulates its sustainable development. Ukraine can take this experience into account in the process of improving its land policy and ensuring the sustainable development 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".