Bibliographic record
Abstract
Following the steps of the European Union, Japan and Canada, the Australian Corporations Legislation Amendment (Audit Enhancement) Act 2012 mandated the publication of transparency reports since 2013 financial year. The regulators hope, by disclosing information about audit firms’ internal governance structure and practices, that the transparency reports could reveal audit quality, differentiate between audit firms and possibly improve audit quality. Our study is the first to quantitatively examine the effectiveness of Australian transparency reports by constructing a Transparency Report Disclosure Score Index and developing a disclosure score for each available transparency report. Using a sample of 1962 firm-year observations, our regression analysis suggests the level of disclosure in transparency reports is positively associated with audit quality of the audit firm. The result indicates that the reports are functioning as intended and auditors can use transparency reports to signal their superior audit quality. However, we also find preliminary evidence that the information asymmetry in the market increased after the publication of transparency reports by measuring the change in bid-ask spread of 482 stocks. We did not find a definitive explanation for the increase in bid-ask spreads due to time limitation and leave it to future researchers. Overall, in addition to complementing and updating studies about transparency reports, our study also has significant regulatory and practical relevance.
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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 | 0.024 | 0.160 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".