The Influence of Audit Risk and Materiality Guidelines on Auditors’ Planning Materiality Assessment
Bibliographic record
Abstract
At present, methods to improve auditquality and auditing decisions are being debated by standard setters andresearchers worldwide. Materiality has been and continues to be a topic ofimportance for auditors.Audit quality is primarilyinfluenced by two factors: the requirements of standard setters and theprofessional judgment of auditors. Materiality judgment is primarily determinedby the subjective judgment of the auditor because there is a lack of clear, standardizedguidelines for such judgments. Thus, the same materiality issue could be judgeddifferently by different auditors. Auditors’ materiality judgments areimportant because they significantly influence what information is recorded inthe accounts, disclosed in financial statements and made available to externalparties for decision-making. The objective of this study is to examine theinfluence that audit risk and quantitative guidelines have on the assessment ofplanning materiality and on the adjustment of material misstatements. We use acase study and conduct an experiment. The study results provide evidence thatthe standardization and implementation of quantitative materiality guidelinesresult in a smaller range of planning materiality judgments. This paperdiscusses the implications of those findings.
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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 | 0.076 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".