Clustering of Regions of the Far East by the Level of Health Determinants
Bibliographic record
Abstract
The aim of the study is to cluster the Far Eastern regions according to the level of their key health determinants, considering demographic, socioeconomic, environmental, and behavioral factors that are specific to each region. The study’s relevance is due to ongoing demographic crises and the pronounced differentiation between regions in terms of key demographic indicators. The methodology included multivariate statistical analysis using t-Distributed Stochastic Neighbour Embedding (t-SNE), k-means clustering, fuzzy-means clustering and self-organized maps, as well as correlation analysis. Clustering of the Far Eastern Federal District’s subjects was performed based on their health determinant levels. Classical and modern approaches were considered as a theoretical basis for defining and assessing these determinants, including international experiences (WHO, the Ottawa Charter), and domestic research. As a result, regions were clustered into groups with different health determinant structures and levels. Key internal and external factors affecting population health were identified. Scientific novelty lies in using a combination of modern clustering methods to normalize indicators, analyze fuzzy affiliations of regions to groups, visualize multivariate data, identify topological relationships between groups, and assess clustering quality using the silhouette coefficient. The use of neural networks and fuzzy logic classification methods significantly improves data analysis quality and clustering results. Practical significance lies in applying the resulting clusters to targeted regional policies, such as targeted resource allocation for prevention programs, improved infrastructure, and monitoring public health systems.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| 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".