Bibliographic record
Abstract
This paper investigates the impact of promoting whistleblowing on audit quality and the efficiency of detecting misstatements. On the one hand, whistleblowing alerts the enforcer to the possibility of a misstatement and intensifies the regulatory effort, thereby incentivizing the auditor to improve audit quality. On the other hand, promoting whistleblowing reduces the enforcer’s investigative effort when there is no whistleblowing allegation, which in turn dampens the auditor’s incentive to enhance audit quality. We demonstrate that, under certain conditions, encouraging more whistleblowing can impair audit quality and reduce detection efficiency. We also examine the socially optimal whistleblowing program, and our analysis implies that the optimal whistleblowing intensity is increasing in investment cost and the quality of the whistleblower’s information. This paper was accepted by Ranjani Krishnan, accounting. Funding: C. Tang acknowledges financial support from the Hong Kong Research Grant Council [Project 16503921]. M. Ye thanks the Canadian Social Sciences and Humanities Research Council for funding support [Grant 435-2022-0093]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2024.04808 .
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 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".