Group Judgment and Decision Making in Auditing: Research in the Time of COVID-19 and Beyond
Bibliographic record
Abstract
SUMMARY The COVID-19 pandemic has fundamentally changed how auditors work and interact with team members and others in the financial reporting process. In particular, there has been a move away from face-to-face interactions to the use of virtual teams, with strong indications many of these changes will remain post-pandemic. We examine the impacts of the pandemic on group judgment and decision making (JDM) research in auditing by reviewing research on auditor interactions with respect to the review process (including coaching), fraud brainstorming, consultations within audit firms, and parties outside the audit firm such as client management and the audit committee. Through the pandemic lens and for each auditor interaction, we consider new research questions for audit JDM researchers to investigate and new ways of addressing existing research questions given these fundamental changes. We also identify potential impacts on research methods used to address these questions during the pandemic and beyond.
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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 | 0.057 | 0.152 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".