A Research Note on the Influence of Outcome Knowledge on Audit Partners' Judgments
Bibliographic record
Abstract
Audit partners may be called upon to evaluate, ex post, the work of another auditor. One example of such an evaluation is a Peer Review. An experiment was conducted that examined the influence of outcome knowledge on the going concern and peer evaluation judgments of 122 audit partners from Canada and the United States. Outcome information was manipulated at three levels—no outcome, negative outcome, and positive outcome information. The results confirm previous research and show that audit partners are subject to the influence of outcome information. Negative outcome information influenced (1) audit partners' assessments of the likelihood of the client's continued existence (hindsight effects), (2) the evaluation of the incumbent auditor's judgment (outcome effects), and (3) judgments of the importance of evidence items. Auditors who received outcome information tended to rate outcome-consistent items of evidence as more important. This suggests that the biasing effect of outcome knowledge operates by acting as a filter that magnifies the relative salience of outcome-consistent information.
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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 | 0.034 | 0.250 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".