INTEGRATED METHODOLOGY FOR ASSESSMENT OF FINANCIAL ABILITY OF LOCAL BUDGETS
Bibliographic record
Abstract
The article addresses the critical issue of ensuring the financial capacity of local budgets, a key factor in the sustainable socio-economic development of Ukraine's regions. The authors propose an integrated methodology for assessing the financial capacity of local budgets, emphasizing its incorporation into a digital system utilizing advanced technologies such as artificial intelligence (AI). Particular attention is paid to the use of coefficients in financial analysis, which ensures objectivity, comparability, and effectiveness of research. These coefficients, as quantitative indicators, enable a comprehensive analysis of budget revenues and expenditures, assessment of financial stability, self-sufficiency of communities, and evaluation of the efficiency of resource utilization. The study provides an in-depth analysis of the budget of the city of Irpin, highlighting trends in the financial stability of the community amidst a crisis caused by military conflict. The analysis revealed both strengths and vulnerabilities, showcasing the role of coefficients in rapid diagnostics of budget performance, identifying problematic areas, and formulating effective solutions for community recovery and development. The article also examines opportunities to adapt international practices, including the Urban Audit program in the European Union, Gender Responsive Budgeting in Sweden, and the Municipal Performance Measurement Program in Canada. The authors stress the need to create an AI-based automated system capable of not only analyzing financial indicators but also generating actionable recommendations for community development. The proposed methodology integrates automated systems with a coefficient-based approach to cluster communities by development level, offering targeted recommendations to improve their financial resilience. This innovation aims to enhance the efficiency of local budget management, ensure transparency in decision-making processes, and promote the socio-economic well-being of the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".