Methodological Approach for Developing Legal Frameworks to Protect Land Relations in Homeland Security
Bibliographic record
Abstract
The main purpose of the article is the formation of a methodological approach that ensures the creation of such a legal framework that would provide protection in the process of land relations to ensure homeland security. Within the scope of the study, the main model and its characteristics were presented. To do this, the scientific task will be to find a new methodological approach to present the main stages of creating a legal framework for the protection of land relations in the system to ensure homeland security. The object of the study is safety and security in land relations. The research methodology involves the application of a methodical approach to modeling the formation of a legal framework for the legal protection of land relations in the homeland security system. The key methods were PEST and IDEF. As a result of the study, a methodological approach was proposed to model the stages of formation of the legal basis for ensuring the legal protection of land relations in the system of homeland security. The scientific novelty of the results obtained lies in the presented methodological approach, which contributed to the increase in the effectiveness of the formation of the legal basis for ensuring the legal protection of land relations in the system of homeland security. The ways of solving the problem presented in the model can be used in the framework of legal activities. The study is limited by taking into account legal and organizational aspects, but not environmental ones.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".