ЦИФРОВА ТРАНСФОРМАЦІЯ ІНСТИТУЦІЙНО-АНАЛІТИЧНОГО ЗАБЕЗПЕЧЕННЯ ПУБЛІЧНИХ ФІНАНСІВ В УМОВАХ ІННОВАЦІЙНОЇ ЕКОНОМІКИ
Bibliographic record
Abstract
Introduction. In the current context of the digital transformation of society, there is a growing need to rethink the role of public finance as a tool not only for fiscal regulation but also for strategic development. Traditional models of budget administration are proving insufficient to ensure transparency, accountability and efficiency in the management of public resources. At the same time, the rapid development of digital technologies, such as blockchain, big data, and artificial intelligence, opens up new opportunities for modernizing the financial system. In this context, the study of the digital transformation of public finance is extremely relevant, as it meets the challenges of the innovation economy and the need to increase trust in public administration. Methods. The methodological basis of the study is a combination of systemic and structural-functional approaches, typological analysis, case method and visualization methods. The empirical basis is based on examples of the implementation of digital platforms in public finance in Ukraine, Georgia, the Baltic States, and Canada. The chronological scope of the study covers 2015-2024. The source base is formed on the basis of data from open budget portals, regulations and international reports (IMF, World Bank, OECD). Results. The article presents a classification of digital solutions into four generations: from open data portals to blockchain platforms with smart contracts. A comparative analysis of the functionality, legal integration and scalability of the OpenBudget, ProZorro and GovChain platforms is carried out. Discussion. The results obtained can be used as an analytical and methodological basis for further research in the field of digital design of budget ecosystems, as well as for the development of regulatory approaches to the integration of decentralized technologies into public financial management.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 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; both teacher heads agree on what is shown here.
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".