Bibliographic record
Abstract
This study examined whether auditors, when they are processing mixed evidence, take into consideration the chronological order of the evidence (giving rise to what this study refers to as a trend effect), or if their evaluations are influenced primarily by the order of presentation (giving rise to what the audit literature refers to as a recency effect). The study's primary objective was to determine whether awareness of the temporal order of evidence would prevent auditors from placing more weight on evidence that they most recently processed (i.e., whether the trend effect dominates the recency effect). Auditors were given an experimental task of going-concern assessment. Auditors evaluating undated mixed evidence exhibited recency effects similar in magnitude to those shown by auditors who were asked to evaluate dated mixed evidence, in which the presentation order was consistent with temporal order. However, auditors evaluating evidence in which temporal order and presentation order were varied orthogonally took into consideration the chronological order of the evidence. This, in turn, led to a significant reduction in the effect of recency. Additional analysis indicates that auditors who evaluated dated mixed evidence chose audit opinions consistent with the trend reflected by the chronology of the evidence.
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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 | 0.020 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".