Substantiating the role and importance of public financial audit in the efficient management of public financial funds through the lens of public sector risks
Bibliographic record
Abstract
In this article, the author starts from the observation that public financial management, including public financial control and audit, must take into account the problems and risks inherent in the public institution model itself, but also those specific to a country and its public sector at a certain time. Applying methods such as the method of scientific abstraction, the method of induction, the method of deduction, analysis and synthesis, the author comes to the conclusion that the safest public sectors, implicitly public institutions, are those from countries with developed democracies, namely - Denmark, Finland and Norway, followed by those from Germany and Canada. The following, according to the level of risks, are some post-socialist countries: Estonia, Slovakia, Slovenia, Lithuania, Latvia, Poland. Hungary, Romania and Bulgaria represent countries with public sectors facing high risks related to corruption and government effectiveness. Armenia and the Republic of Moldova face high risks at the level of the sector and public institutions. This finding suggests the conclusion about the lack of efficiency and effectiveness of the internal and external public financial audit activity.
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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 | 0.017 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".