Public auditing: What impact does the quality of the institutional framework have on the level of corruption?
Bibliographic record
Abstract
Supreme audit institutions (SAIs) are a component of a nation's institutional system. This article defines the concept of an institutional anti-corruption system centered on the SAI through four main characteristics: independence, accountability, mandate and collaboration. The article aims to assess the impact that the quality of the anti-corruption system has on perceived levels of corruption. Data from the 2019 International Budget Partnership Open Budget Survey covering 117 countries are used for this purpose. The regression results show that the quality of the institutional anti-corruption system centered on the SAI is associated with a low level of perceived corruption. However, other elements must be implemented to create an anti-corruption environment, such as citizens’ involvement as controlling actors. Points for practitioners It is important that policy makers recognize and leverage the potential of SAIs in reducing corruption. Contemporary governance and the complexity of corruption require the protection of SAI's independence, but also call for the establishment of collaborative mechanisms that engage civil society and the media.
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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 | 0.021 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".