State Legal Policy for Planning Sustainable Development of Recreational Areas: Environmental Risk Assessment for Open Socio-Economic Systems
Bibliographic record
Abstract
In the context of global challenges such as climate change, biodiversity loss and environmental pollution, environmental risk assessment is becoming a key tool in planning the state legal policy for the sustainable development of recreation areas.The main purpose of the article is to form a methodological approach to assessing options for implementing state legal policy on sustainable development in recreation areas.The object of study is the recreation areas of Ukraine.The scientific task involves presenting the author's vision of assessing options for ensuring sustainable development in recreation areas.For our purposes, one of the methods of systems analysis was applied -the BOCR method.The BOCR method, including benefits, opportunities, costs and risks, offers a comprehensive approach to evaluating projects, providing a deep understanding of their potential and challenges to make informed decisions.It is commonly used to evaluate the effectiveness of sustainable development projects.As a result of the study, a methodological approach to assessing options for ensuring sustainable development was developed, the results of which made it possible to obtain an information basis for the development and implementation of state legal policy in Ukraine.The existing hierarchical BOCR assessment model and the calculations performed have demonstrated the higher efficiency of the "green" mechanism for planning sustainable development with an emphasis on supporting eco-business in these areas.The introduction of a green planning mechanism promotes environmental sustainability and socio-economic development, while at the same time providing a state legal policy framework for sustainable management of recreational areas.The study has limitations in terms of taking into account the specifics of zones exclusively in Ukraine.Prospects for further research are aimed at expanding and exploring new recreation areas across Europe.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".