Features of Providing Sustainable Regional Development in the Conditions of Globalization Challenges
Bibliographic record
Abstract
The article reveals the peculiarities of ensuring the sustainable development of regions in the face of globalization challenges.These challenges are manifested in the growth of the environmental crisis, the shortage of traditional resources for production, the strengthening of demographic imbalances, the growth of social inequality, the deformation of market structures, and the crisis of the efficiency of capital investments, among others.The timeliness of the research is determined by modern globalization challenges of social development, which have a paradigmatic direction toward the implementation of the sustainable development concept.The purpose of the study is to substantiate the areas of sustainable development of regions based on the identification of the most influential indicators on the comprehensive integrated index of sustainable development using the matrix game method.The methodological basis of the research is a systematic approach, which allows for the study of regions in the context of globalization challenges as part of the system and the application of mathematical tools such as correlation analysis, multiple regression, simulation modeling, and the matrix game method.The authors have improved the methodical approach to assessing the sustainable development of regions in the face of globalization challenges.This involves using the matrix game method to determine the most optimal strategy for the region's sustainable development by identifying the most influential indicators that ensure an increase in the integrated index of sustainable development in the future.The methodical approach was tested using examples from regions of Ukraine.The analysis results for two Ukrainian regions identified the most influential factor on the sustainable development of each region and enabled modeling of the integrated index of sustainable development considering this influence, which demonstrated positive dynamics in the growth of the integrated index of sustainable development for the regions.
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".