The Impact of the Requirements of International Audit Standard No (300): Planning for Auditing Financial Statements on the Quality of the Audit Report
Bibliographic record
Abstract
Purpose: This study aimed to demonstrate the impact of the requirements of the International Auditing Standard No. (300): Planning the auditing of financial statements on the quality of the report performed by the external auditor. Design / Methodology / Approach: The descriptive statistical method was used, the data was collected through a questionnaire developed by the researcher, which targeted (113) individuals of the external auditors. The study included six axes, five of which were allocated to measure the impact of the requirements of the audit standard No. (300), while the sixth axis was devoted to measure the quality of the auditing report. Results: The study found that there is a statistically significant relation with the requirements of the International Auditing Standard No. (300): Planning the auditing of financial statements on the quality of the report of the external auditor, where the axis of planning activities had the most significant impact on the quality of the audit report, while the additional considerations were less impactful in the auditing process. Originality/ Value: According to the researcher knowledge, this study is considered as the first one which dealt with the variables of the study combined; this will provide a solid ground for researchers to motivate themselves to conduct further research and studies on these variables.
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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 | 0.027 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".