Can Auditors Predict the Choices Made by Other Auditors?
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
An implicit assumption of prior literature on strategic behavior of auditors is that auditors know the preferences of their colleagues. In this study, we conduct an experiment to investigate the validity of this assumption. In our experiment, we match a manager with a pair of top and mediocre audit seniors, as assessed by their firm. Each auditor predicts the choices that will be made by other auditors on two tasks that differ in their level of ambiguity. Our results show no difference in the accuracy among managers, top seniors, and mediocre seniors when they predict the choices made by specific individual auditors for both tasks. When predicting the number of managers and seniors who will choose a specific option on the high‐ambiguity task, managers outperform top seniors, who in turn outperform mediocre seniors. For the low‐ambiguity task, we find no difference among managers, top seniors, and mediocre seniors. Our results provide some limited support for models of strategic auditor behavior, and indicate that the ability to predict the choices of others is a dimension of an auditor’s expertise.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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