“Order Effects” Revisited: The Importance of Chronology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it