Tall Tales Across Time: Narrative Analysis of True and False Allegations
Bibliographic record
Abstract
Abstract Little consensus exists regarding how the details of truthful and false allegations of traumatic victimisation may change over short and long time intervals, yet this cue is utilised in the assessment of witness, victim and suspect credibility. The present study involved a narrative analysis of the details written within 147 sets of allegation statements across both short‐term (~3 months) and long‐term (~6 months) intervals. Overall results indicated that true allegations contained more consistent details, omissions and commissions, although the rates of change over time were variable. These changes appear to result from natural variations in memory and recall over time. However, direct contradictions (inconsistent details) were more prevalent in false allegations, and these claims were more stable over time, suggesting ‘script‐like’ processing. These results have implications for our understanding of testimonial alterations and how determinations of veracity are influenced by statement details. Copyright © 2014 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".