The effect of board characteristics and audit committee characteristics on audit quality
Bibliographic record
Abstract
The issues of audit quality and audit committee have received huge consideration from the auditing profession, the general public population and the government controllers particularly after the prominent corporate outrages in firms like Enron, Global Crossing, Tyco, and WorldCom. These concerns discourage investors to invest in foreign and local businesses. The primary objective of the current study is to explore the impact of internal and external governance mechanisms such as board size, audit committee independence, audit committee expertise, and audit committee meetings on the quality of audit in selected firms. The study is carried out on a sample of Iraqi nonfinancial firms. The dependent variable is the audit quality measured as a dummy variable and it receives 1 if a firm receives audit services of big five auditing firms and zero, otherwise. To achieve the research objectives the study uses logit regression technique. The results indicate that there was a positive relationship between audit quality and the percentage of non-executive directors in the audit committee. The findings of the current study will be helpful for policymakers, researchers, accountants, financial experts, and audit practitioners in understanding the importance of the concept of audit quality and the key factors which affect the audit quality of any non-financial firms in Iraq.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".