Three Essays on Key Audit Matters Dissimilarity
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
My dissertation consists of three essays related to the Key Audit Matters (KAM) section in audit reports. KAM disclosures have been implemented to enhance the communicative value of audit reports. KAMs reflect the greatest risks of material misstatements encountered during the audit process. Critics fear that KAMs would be boilerplate and standardized. I develop measures of dissimilarity to capture specific information in KAMs. These measures reflect differences in words written by auditors for the same type of KAM. I first detail client and audit firm characteristics associated with client-specific (dissimilar) information in KAMs. I then link the KAM and audit risks components. Finally, I examine the informativeness of auditors’ risk disclosures. My Thesis contributes to the KAM literature by providing a granular analysis of the content of KAM disclosures. I supplement the audit literature and highlight the importance of examining the two KAM components separately.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.020 |
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