Toward an Archival Measure of the Likelihood of Auditor–Client Management Negotiation: An Exploration of the Audit Lag Measures Conjecture*
Bibliographic record
Abstract
ABSTRACT Salterio (2012) hypothesized that adaptations of an audit efficiency measure, audit report lag (the length of time between the financial statement year‐end date and the auditor's report date), could provide measures of underlying auditor–client management (ACM) negotiation likelihood. Salterio argued that these measures would enable archival researchers to examine issues that heretofore were the exclusive domain of experimental and field researchers. Using an audit report lag measure and a measure of abnormal audit report lag lags (the residual based on audit report lag determinants model), we show that a larger lag is associated with higher audit fees after controlling for other known determinants of audit fees. We also show that larger lags are associated with higher levels of discretionary accruals—that is, lower accrual quality. Based on our findings, we suggest that there is support for Salterio's hypothesis that audit report lags and abnormal audit report provide valid archival proxies for the differences in year‐end ACM negotiation likelihood. We suggest that this proxy will allow researchers to study issues related to published accounting numbers in light of whether negotiations are likely to have occurred in addition to providing regulators and others the means to determine what clients of audit firms are more likely to have different types of ACM relationships.
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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 | 0.027 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".