Legal Regulation of Land Relations in the Context of Canadian Legislation
Bibliographic record
Abstract
The relevance of the study is conditioned by the need to analyse international practices of legal regulation of land matters, in particular on the example of the Canadian experience, to update and further implement Ukrainian legislation. The purpose of the study is to identify the features of the legislative regulation of land relations in Canada, Ukraine, and other countries. The methods used to investigate the selected topic include: dialectical, formalisation, legal, formal and legal, hermeneutic, logical-legal, systemic, structural and functional, axiomatic, induction and deduction, analysis and synthesis. The study identified the basic concepts, terms and phenomena used in the field of land regulation in Canada and other countries; the concept of “land relations” is characterised, the objects and subjects of land relations are defined; an analysis of the legal framework governing land relations in Canada and the peculiarities of such regulation; features of regulation and implementation of land relations in Ukraine and such countries as Great Britain, Germany, France, Australia, and New Zealand are described; the list of regulations is compiled based on which regulation of land relations is carried out. The provisions highlighted in the paper are of practical value primarily for entities whose activities are aimed at regulating and controlling land relations, persons whose rights are directly or indirectly covered by the land sphere and fall under the regulation in this area
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".