ARABIAN ONLINE OPEN ACCESS JOURNALS : A STUDY ON DOAJ
Bibliographic record
Abstract
This study investigates the effects of material weaknesses from auditing standards and of material misstatement from accounting standards on the audit sanctions severity. Using a unique database in the period 1983 – 2015, we find mixed results. Among the auditing standards, Internal Control Weaknesses lead to more severe audit sanctions than Quality Control, Other Auditors, Reporting and Audit Opinion Material Weaknesses Audit Sanctions, and to less severe audit sanctions than Professional Skepticism and Substantial Procedures. Among accounting standards, Fair Value misstatements are associated with more severe audit sanctions than Long-term investment, bank debts, and liquidity errors, and with lower severity of audit sanctions than Account receivables. Taken together, these findings suggest two main determinants of audit sanctions severity that auditors and accountants need to be aware: the area of internal control deficiencies and the area of fair value measurement. From these results, we learn that accounting and auditing standards errors have different likelihood of audit sanctions, and that auditors that aim to avoid sanctions need to invest mainly in the internal control assurance and in the fair value items of the financial reporting.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".