Does Recent Academic Research Support Changes to Audit Reporting Standards?
Bibliographic record
Abstract
SYNOPSIS The Public Company Accounting Oversight Board (PCAOB), the International Auditing and Assurance Standards Board (IAASB), and the U.K. Financial Reporting Council (FRC) have proposed or approved standards that significantly change the independent auditor's report. These initiatives require the auditor to make additional disclosures intended to close the information gap; that is, the gap between the information users desire and the information available through the audited financial statements, other corporate disclosures, and the auditor's report. They are also intended to improve the relevancy of the auditor's report. We augment prior academic research by providing standard setters with an updated synthesis of relevant research. More importantly, we provide an assessment of whether the changes are likely to close the information gap, which is important to financial market participants and other stakeholders in the audit reporting process. Also, we identify areas where there seems to be a lack of sufficient research. These results are of interest to all stakeholders in the audit reporting process, as the changes to the auditor's report are fundamental. Additionally, our summaries of research on the auditor's report highlight where there is limited research or inconsistent results, which will help academics identify important opportunities for future research.
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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 | 0.053 | 0.310 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.037 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".