Joint Audit: Issues and Challenges for Researchers and Policy-Makers
Bibliographic record
Abstract
The publication of the European Commission Green Paper, ‘Audit Policy: Lessons from the Crisis’ in October 2010, has stirred up a lively debate on the role of joint audits. This literature review identifies and evaluates, for the benefit of future research and regulators, existing evidence about joint audits. We find limited empirical support to suggest that joint audits lead to increased audit quality, but some empirical support to suggest that joint audits lead to additional costs. Overall, this paper indicates that joint audit should be seen as a mechanism that is embedded in a broader institutional context and not be considered in isolation from other factors that might impact the audit market. The results indicate that various country-level characteristics are simultaneously at play. While joint audits can potentially enhance the audit market competition by allowing smaller audit firms to maintain larger market shares, the related impact on audit quality has not yet been clearly demonstrated and thus provides a promising avenue 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 | 0.258 | 0.349 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.017 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.041 | 0.046 |
| Open science | 0.008 | 0.019 |
| Research integrity | 0.033 | 0.019 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".