Dissimilarity in Key Audit Matters: Determinants and Consequences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Key Audit Matters (KAMs) provide opportunities to assess qualitative auditor disclosures. Research shows that KAM disclosures become boilerplate over time and auditors exhibit herd behavior in writing them. Using textual analysis, I examine within-industry differences in the wording used by auditors of different companies for disclosures of the same type of KAMs in the same fiscal year. I examine the entire KAM as well as its two components—the risk description and the auditors’ response—using three dissimilarity measures capturing client-specific information. I analyze factors related to KAM dissimilarity and its association with audit quality and audit report lag. KAM disclosures are specific to each audit engagement and vary by audit partner. My findings show that KAM dissimilarity is associated with lower audit quality and a longer audit report lag. My results have practical implications for standard-setters as they seek to understand factors related to the content of KAM disclosures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it