Impact of internal control systems on financial irregularities in local governments
Bibliographic record
Abstract
This study investigates the impact of internal control systems on financial irregularities in 11 municipalities spread across 7 regions in Ghana. Using a descriptive research design and a quantitative approach, data was collected through a structured questionnaire. The study established challenges hindering the implementation of internal control systems as; inadequate resource allocation, unsatisfactory staff rewards, unnoticed misconduct, and collusion among staff. The findings provide valuable insights for management and policymakers in the public sector, offering recommendations to enhance operations by strengthening internal control systems. Recommendations include adopting best practices, providing additional resources and training, integrating internal controls with risk management, conducting regular staff performance audits, and enacting laws and regulations to enforce internal control implementation and report financial irregularities. The study depends hugely on self-reported quantitative information, audit reports, local government reports, and literature. Future studies should consider mixed methods approach to the study. This will offer an opportunity to collect qualitative data to explain the observed patterns.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".