Impact of Public Sector Auditing in Promoting Accountability and Trasparency in Nigeria
Bibliographic record
Abstract
Accountability and Transparency has over the years been recognized as instruments of reduction of corruption at all levels of public sector. A lack of transparency and accountability in the public sector presents a major risk to the efficiency of the capital markets, financial stability, long term economic sustainability, economic growth and development. Unfortunately, the issue of accountability is a basic and fundamental problem in our country Nigeria. This is as a result of the high rate of corruption embedded in virtually all sector of our economy Nigeria. Going by the increase in democratization and concern about corruption, citizens are demanding from the government accountability and transparency by being well informed about what the government intends to achieve and what it has actually accomplished. Since public sector financial statement is the medium of information of government activities, the public is demanding audit reports in order to access the performance of those entrusted with public sector resources. This therefore implies that proper audit plays a significant role in promoting accountability. This study therefore seeks to examine the role of public sector audit in enhancing accountability and transparency in the public sector while bringing about a reduction in the level of corruption in the country.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".