Sidetracked by emotion: Observers’ ability to discriminate genuine and fabricated sexual assault allegations
Bibliographic record
Abstract
Purpose. Assessing the credibility of reports of sexual victimization – often in the absence of corroboration – presents a significant challenge for legal decision makers. This study examined the accuracy of observers in discriminating genuine and fabricated sexual assault allegations. Further, we examined whether individual differences and cue utilization strategies influenced deception detection accuracy. Methods. Observers ( N = 119) evaluated eight (four truthful and four deceptive) written allegations of sexual assault (counterbalanced), and completed a Credibility Assessment Questionnaire (CAQ) and individual differences measures. Results. Results indicated that overall accuracy was below chance ( M = 45.3%), and a truth bias was evidenced. Examining the Big Five personality traits, we found that openness to experience and neuroticism were positively associated with accuracy, whereas extraversion was negatively related to accuracy. Further, judgement confidence was negatively associated with accuracy. Conclusions. The present study offers insights into observers’ perceptions of credibility regarding real‐life sexual assault allegations. Implications for legal decision making are discussed.
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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 | 0.002 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".