Monitoring the Sustainable Development: Which Model is Most Effective for Ukraine?
Bibliographic record
Abstract
The article surveys the principles of building a sustainable development monitoring system, analyzes international models of its implementation, and substantiates ways to adapt these models to Ukrainian realities. The article also examines examples of centralized, decentralized, and hybrid approaches used in China, Canada, the European Union, and Estonia. The advantages and limitations of each model are analyzed in terms of the efficiency of data collection and processing, public engagement, flexibility in selecting indicators, and the adequacy to the needs of different levels of governance. Based on the carried out analysis, proposals have been developed for the introduction of a hybrid monitoring model in Ukraine, which combines a centralized sustainable development framework with the ability to localize indicators according to the socioeconomic characteristics of regions and communities. The introduction of such a model will enhance the relevance of data, ensure transparency in decision-making, and create a system that is responsive to the needs of specific territories. Firstly, this will facilitate strengthened inter-level interaction among central authorities, regional administrations, and local communities, providing a unified analytical foundation for sustainable development planning. Secondly, the use of digital platforms for data collection and visualization will enable the automation of monitoring processes, reduce administrative burdens, and improve public access to information. Thirdly, a system that considers local needs will provide a more accurate measurement of progress towards the Sustainable Development Goals (SDGs) and contribute to the effectiveness of regional strategies. The prospects for further research lie in the formation of a list of optimal indicators for each level of management, the development of IT infrastructure for data collection in communities, the enhancement of institutional capacity in regions, and the integration of the Ukrainian sustainable development monitoring system into the European analytical space. The implementation of the proposed model will allow for the establishment of a modern, adaptive, and transparent system for monitoring sustainable development in Ukraine, capable of effectively supporting decision-making in the field of socioeconomic planning.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".