Investigating the Application of Digital Tools for Information Management in Financial Control: Evidence from Bulgaria
Bibliographic record
Abstract
This paper discusses the application of digital information management tools in the context of financial control. In Bulgaria, such research is innovative as it is the first time that digital transformation in crucial financial control institutions, which influence the formation of the revenue part of the state budget and the spending of public funds, has been studied. The study aims to answer the research question of to what extent the application of digital tools in financial control improves its effectiveness. It analyses how modern technologies improve the efficiency and accuracy of information used in financial control institutions. The authors examine the impact of digital tools, such as database management systems, business analytics platforms, and electronic document management tools, on collecting, analyzing, and managing financial and non-financial information. The study uses descriptive statistics and a correlation analysis, which significantly contributes to establishing the relationship between implemented digital tools and improvements in financial control procedures. The results show that despite the conditions created for digitalization in financial control institutions, digital tools are used to a limited extent in the information management process. The study emphasizes the need for continuous investment in digital technologies and training to maximize the benefits of their application in financial control.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".