AUDIT COMMITTEE CHARACTERISTICS, REGULATORY CHANGES AND FINANCIAL REPORTING QUALITY IN IRAQ: SOME LESSONS FROM SOX ACT
Bibliographic record
Abstract
This study aims to examine the impact of audit committee characteristics such as audit committee size, audit committee meetings, audit committee independence, and the financial expertise of audit committee on the quality of financial reporting in non-financial firms operating in Iraq. In addition to that the study also examines the direct and moderating role of regulatory changes at the nexus between audit committee characteristics and quality of financial reporting in the context of non-financial firms in Iraq. The particular focus of the study is on the implications of Sarbanes-Oxley Act in Iraq. For this purpose, the author selects 170 organizations as the study sample which comprises a total of 850 firm-year observations. For further analysis, only 575 organizational-years observations are included. The multiple regression model is used to analyze this data. Referring to the resource dependence theory, results indicate that characteristics of an Audit Committee are highly resourceful, which leads to enhancement of the financial reporting quality because of the expertise, greater skills, and shared experiences. The regulatory changes also appear to be a significant direct and intervening factor in the relationship between the characteristics of an Audit Committee and reporting quality in the non-financial firms of 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".