Bibliographic record
Abstract
Abstract Accounting comparability among peer firms in the same industry reflects the similarity and the relatedness of firms’ operating environments and financial reporting. From the perspectives of “inherent audit risk” and “external information efficiency,” comparability is helpful for auditors in assessing client audit risk and lowers the costs of information acquisition, processing, and testing. I posit that the availability of information about comparable clients helps improve audit efficiency and accuracy. Empirical results show that comparability is negatively related to audit effort (surrogated by audit fees and audit delay). Moreover, comparability is negatively associated with the likelihood of audit opinion errors. These findings are robust to different specifications of regression models, particularly for the “endogeneity” issues due to the possible reverse causality that auditor style might influence client firms’ comparability. In sum, the study shows that accounting comparability enhances the utility of accounting information for external audits.
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.
How this classification was reachedexpand
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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".