Bibliographic record
Abstract
This study explores audit quality in ASEAN from an analysis of the legal environment faced by statutory auditors. First, it provides an overview of the national laws, regulations, professional codes and standards defining the legal environment. Second, it provides an economic analysis of the main differences among countries and relates those differences to the functioning of the audit markets, with a potential for uneven audit quality in the region. Data were collected with questionnaires from national representatives of four Big Five firms, and accuracy of the information was reviewed by 15 governmental and professional bodies responsible for regulating the auditing profession in ASEAN. Analysis of the data revealed a diverse legal environment among the ASEAN countries possibly creating a climate of differential audit quality. Many differences were observed in the competence requirements of auditors, the requirements regarding the conduct of statutory audits, and the reporting obligations. Further, audit quality in some countries is seriously compromised due to a lack of rules ensuring auditors' independence. Finally, some of the liability regimes in ASEAN do not provide an incentive for statutory auditors to provide quality audit services. Several recommendations are made to improve the legal environment by bringing the national laws and regulations in line with international standards of auditing which would result in a more uniform audit quality throughout ASEAN.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".