Managing Perceptions of Technical Competence: How Well Do Auditors Know How Others View Them?*
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
Abstract We investigate factors that influence an auditor's accuracy in knowing how subordinates, peers, and superiors view his or her own technical competence (metaperception). Extant literature on reputation management in auditing contexts depicts preparers of audit workpapers as strategic agents (subordinates) who stylize workpapers and engage in behaviors that enhance their reputations with reviewers (superiors). These superiors, in turn, are represented as strategically engaging in coping behaviors in response to such stylization attempts. One of the necessary conditions for auditors to enhance their reputations on a sustainable basis is accurate metaperception. We report the results of an experiment that investigates determinants of auditors' metaperception accuracy. Our participants comprise teams of audit partners, managers, and seniors who work together in the field. Each auditor performs two tasks of varying complexity and then predicts whether other team members can accurately perform the task and how other team members assess his or her performance on the tasks. Results show that accuracy in knowing what others think of one's technical proficiency (metaperception) is generally high, particularly when the predictor auditors are partners and managers; however, metaperception accuracy is asymmetric and varies depending on the predictor auditor, the target auditor being predicted, and task complexity. Implications are discussed.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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