INTERNATIONAL EXPERIENCE IN SUSTAINABLE LEGAL MANAGEMENT OF FOREST FUND LANDS AND ITS SIGNIFICANCE FOR UZBEKISTAN
Bibliographic record
Abstract
The article provides an in-depth comparative legal analysis of the legal regulation of the use of forest fund lands using examples from foreign countries: Germany, the USA, Canada, and Russia. The research covers various models of forest resource management, including the ratio of state and private ownership, legal mechanisms for leasing forest plots, and forms of state participation in regulating and protecting forests. It is emphasized that, despite the differences in institutional approaches, the legislative systems of the countries under consideration are based on common principles of sustainable forest management, maintaining ecological balance, and ensuring economic efficiency. Special attention is paid to identifying both specific features and similar trends. In Germany, private ownership of forest resources prevails with active state support. In Canada and Russia, the model of state ownership with a multi-level regulation system prevails. In the USA, there is a mixed system characterized by a high share of private owners and developed coordination between federal and local authorities. Legislative mechanisms for monitoring, stimulating the rational use of forests, and environmental protection were analyzed separately. It was concluded that studying and adapting foreign experience can significantly contribute to improving Uzbekistan’s forest legislation. Integrating environmental, economic, and social aspects in the development of forest policy will increase the effectiveness and sustainability of the national forest management system.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".