Clustering of Regions of the Far East by the Level of Health Determinants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.032 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it