Classification of Regions of Ukraine by The Level of Social Tension
Bibliographic record
Abstract
The analysis of indicators that reflect changes in the social, economic and political spheres in recent years has shown their significant deterioration and the possibility of growing social tensions in the regions of Ukraine. The purpose of the study is to classify the regions of Ukraine according to the level of formation of social tensions and to determine anticipative measures aimed at preventing the creation of crisis situations. The article proposes a methodical approach to the classification of regions using the methods of cluster, discriminant analysis and analysis of variance according to the level of social tension, which includes two main stages: substantiation of the system of socio-economic indicators characterizing the level of social tension; selection and substantiation of models of classification of the regions. Within the first stage of the methodical approach the system of indicators which reflect changes in social, economic and political spheres of Ukraine in modern transformational conditions was constructed. Within the framework of the second stage of the methodical approach on the basis of cluster analysis the classification of regions according to the level of formation of social tension was carried out. The classes of regions were selected: with a low level of formation of social tension; with an intensified level of formation of social tension; with a high level of formation of social tension. The results of the study showed that the number of regions in the class with a high level of social tension is constantly growing and, unfortunately, the number of regions with high socio-economic development is decreasing. The classification of regions made it possible to determine the list of preventive measures that can reduce the losses of the state associated with the containment of possible crises in the social sphere. However, the article also states that such a list of activities should take into account the specifics of the region that is part of each class
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".