Possibilities of Land Management Planning and Development to Achieve the Principles of Sustainable Development: The Relationship Between Legal Regulation and Institutional Support Models in the Context of Digitalization Development
Bibliographic record
Abstract
Modern challenges of sustainable development require improving the mechanisms of legal and institutional regulation of land resources in the context of digital transformation.This is necessary to improve land management efficiency while ensuring a balance of land use's environmental, economic, and social aspects.We selected several countries (Russia, Kazakhstan, Uzbekistan, Kyrgyzstan, and Tajikistan) as the object of research to determine optimal models for regulating land resources.The methodology includes a comparative legal analysis of the regulatory framework of the selected countries, expert interviews (n=42), and focus groups with experts in land law and digitalization of public administration.The authors have found that in the countries studied, the effectiveness of digital transformation of land management is determined not only by technological solutions but also by the degree of harmonization of national legislation with international standards of sustainable development and the availability of institutional mechanisms for interdepartmental integration and legal protection of land data.There are two main trends: the formation of comprehensive legal regulation (Russia, Kazakhstan) and fragmentary legislation updating (Uzbekistan, Kyrgyzstan, Tajikistan).Ultimately, based on the results obtained on the relationship between legal regulation, institutional mechanisms, and digitalization of land management, the authors have proposed recommendations for planning and developing land management to achieve the principles of sustainable development for each country.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".