The Efficiency of Applying the Internal Control Components Based on COSO Framework to Transparently Carry out Tasks and Services, Ensure Integrity and Enhance Quality and Efficiency: Case Study - The Greater Amman Municipality
Bibliographic record
Abstract
This study aimed to measure the efficiency of employing the internal control components based on COSO framework to transparently carry out tasks and services, ensure integrity and enhance quality and efficiency, so as to contribute to promoting the adoption of internal control components based on the COSO framework, applying them and analyzing their efficiency in performing tasks transparently, ensuring integrity and enhancing quality and efficiency, particularly with the Amman`s Municipality efforts to create a directorate that operates the internal control while ensuring the integrity of the proceedings, carrying out tasks and services transparently and boosting citizen's confidence in the Greater Amman Municipality resolutions. One of the main findings of the study would be in the fact that the independent study variables represented in the internal audit based on the COSO framework has a highly positive impact in performing tasks transparently to ensure integrity, boost quality and efficiency at the Greater Amman Municipality. Results show that the communications systems component was the most highly applicable, followed by the follow-up and control procedures and activities, whereas the control environment came third, followed by risks and response procedures identification and assessment, and finally came the appropriate follow-up component in the fifth place.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".