Socio-Economic Transformations of Small Rural Settlements of Zakarpattia Amidst Decentralisation
Bibliographic record
Abstract
The study analyses the key parameters of the population of small rural settlements of the Zakarpatska Oblast amidst economic transformation. Small settlements are described by low population density, which in some rural settlements is three times lower than the regional average. Over the last period there has been a tendency to increase the number of small rural settlements with a population of less than 150 people. Depopulation of villages creates conditions when it is unprofitable to maintain educational, healthcare, cultural institutions, etc. The current situation leads to the destruction of the infrastructure of the village, which is already in decline. The purpose of the study is to analyse the socio-economic development of small rural settlements in the region amidst decentralization. The main attention is paid to the study of problems that describe the quality of life of the rural population – analysis of the main trends of demographic reproduction, the development and effectiveness of new legal forms of management in rural areas amidst the intensifying market relations. The sectoral structure of the economy of rural settlements is analysed, the study substantiates the offers directed on integration of small rural commodity producers in the integrated agro-industrial structures, development of the rural industry, establishment of cooperative movement in the village. The level of satisfaction of the rural population with the main objects of social infrastructure is investigated on the materials of sociological monitoring of the rural population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".