Bibliometric Framing of Research Trends Regarding Public Sector Auditing to Fight Corruption and Prevent Fraud
Bibliographic record
Abstract
Combating corruption is an important objective of the United Nations Sustainable Development Group, with the aim of helping public institutions to act in the interest of citizens. To ensure this objective is met, the spending of public money is controlled by the supreme audit institutions of each country. The objective of this paper is to identify trends in and approaches to the field of auditing in the public sector to combat corruption and prevent fraud. To achieve the proposed objective, a bibliometric analysis of papers published in the journals indexed in Web of Science Clarivate Analytics for the period 2003–2022 was carried out; selection criteria was based on instances of the keywords “public audit fraud”, “supreme institution”, and “fraud” appearing in a sample of 528 articles. The results showed that there was a research interest in this field, with the trend being more pronounced since 2017. The main topics addressed were those related to the performance audit and the fight against corruption, and the most relevant studies were conducted on samples from Nordic European countries. Thus, it is confirmed that the external audit in public sector is an important factor in combating the phenomenon of corruption in the public sector, both by detecting fraud and by offering recommendations aimed at making the activity of this sector more efficient.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.012 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".