Retrospective Result Analysis of Land Reforms in the Russian Federation
Bibliographic record
Abstract
The existing system of land relations requires better organizational and economic mechanisms, search for new ways to increase its efficiency and competitiveness. Land reforms in Russia focused on land privatization transformed the entire system of land relations. Undeveloped state regulation and the difficult financial situation discouraged most agricultural producers from reproducing land resources. The present paper aims to conduct a post-event analysis of land tenure strategies, regulatory and legal acts and scientific and methodological support of land reforms in Russia. The existing approaches to studying theoretical and methodological issues of land relations regulation, shortcomings in methodological and legal support, the practical need for new methods and tools for effective land management in the agricultural sector have predetermined the relevance and significance of the research topic. Research target is the territory of the Russian Federation, the creation of a system of land ownership and land use adapted to the market economy. The study involved analyzing statistical data on agricultural production in combination with quantitative and qualitative indicators of land resources. The work provides a wealth of experience in land transformations, planning and forecasting the Russian territories' socio-economic development, and working out land management methods, which can be applied in other countries.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".