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Monitoring the Sustainable Development: Which Model is Most Effective for Ukraine?

2025· article· en· W4414853146 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBusiness Inform · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsFlexibility (engineering)Transparency (behavior)Sustainable developmentData collectionAutomationRelevance (law)Sustainability

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.807
Threshold uncertainty score0.572

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.223
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it