SMART Management of Sustainable Development of the Region in the Context of Globalization
Bibliographic record
Abstract
The main purpose of the study is to determine the main stages of SMART management of sustainable development of the region for the countries of Eastern Europe in the context of globalization. The issue of implementing a SMART management system today is relevant and critical for the further optimal functioning of the regional management system. The process of implementing SMART management itself is complex and complex, given this, there is a real need to find new methods for systematizing this process in the regional management system. The methodology involves the application of the method of graphic representation of the functional achievement of the goal. This methodology allows you to depict the main stages of achieving the goals in the beat way. Based on the results of the study, we have formed a graphically step-by-step model of effective SMART management of sustainable development of the region for the countries of Eastern Europe under the influence of globalization. As a result of the formation of this model, we have algorithmized and systematized the process of implementing SMART management of sustainable development of the region for the countries of Eastern Europe in the context of globalization. The use of this model will facilitate the adaptation of regional governance systems to a qualitatively new type of management. The study has limitations and concerns limited access to the socio-economic and sustainable development of the regions. We have taken only the regions of Eastern Europe as an example, but in the future we should expand our graphically step-by-step model of effective SMART management of sustainable development of the region for the countries of Eastern Europe under the influence of globalization for the regions of the whole world.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".