The Impact of Accountability on Auditors' Processing of Nondiagnostic Evidence
Bibliographic record
Abstract
Previous research on auditors' processing of nondiagnostic evidence has demonstrated the existence of a dilution effect - the tendency to underreact to diagnostic information when accompanied by nondiagnostic information. Prior audit studies find that accountability, a prominent feature in audit settings, does not affect the magnitude of the dilution effect exhibited by auditors. Based on more recent theories avout accountability, this line of research is extended by exploring whether (1) the dilution effect previously identified is a robust phenomenon that can be replicated, (2) accountability has an impact on both the frequency and magnitude of dilution effect, and (3) the impact of accountability on both the frequency and magnitide of dilution effect is conditional on the degree of accountability experienced by the participants through various reporting levels. The experimental results from a sample of internal auditors provide evidence supporting the first two propositions; however, the results related to reporting levels are not significant. A discussion of the implications of these findings for audit research and practice follows.
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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 | 0.033 | 0.305 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".