Key Audit Matters Between Auditors and Auditees in Middle East and North Africa
Bibliographic record
Abstract
This study investigates the impact of auditor- and auditee-specific features on key audit matter disclosures in auditors’ reports. It focuses on client factors, including debt, profitability, financial distress, and audit factors, including auditor size, rotation, and opinion. A direct extraction of audit reports from different markets in Middle East and North Africa covering three years from 2020 to 2022 was carried out. A content analysis of the annual reports regarding key audit matters, client-specific characteristics, and auditor characteristics was performed in this research. The results of this study show that key audit matters are not correlated to profitability and financial distress, while the debt ratio is significantly related to the number of key audit matters. The results also indicate that audit rotation and opinion have a significant explanatory effect on key audit matters as the coefficients of both independent variables are positive and statistically significant while the size of the audit firm is not related to the number of key audit matters.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".