State Regulation of Digital Technologies for Sustainable Development and Territorial Planning
Bibliographic record
Abstract
In the current context, the Eurasian Economic Union (EAEU) faces the challenge of ensuring sustainable development and effective state regulation. One key issue is to foster coordination and cooperation among different regions and EAEU member states in the development of digital technologies. This study aims to propose directions for state regulation of digital technologies and their integration into a comprehensive mechanism for sustainable development and planning, within the concept of smart territories. We introduce the concept of smart territories as an outcome of sustainable development and planning, enabled by the adoption of digital technologies. Based on an expert survey, we identify key parameters for sustainable development and planning in smart territories, including stakeholder interests, modern challenges for territorial communities, and success factors. We conclude that the integration of digital technologies into smart territories can provide new standards for quality of life and economic development, while preserving the natural resources of these territories. Based on a balanced use of resources, our study highlights the need for a comprehensive strategy for the sustainable development of smart territories, addressing economic, social, and environmental issues.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".