Perceptions of Allegations of Repeated Victimization: The Roles of Event Frequency, Language Specificity, and Disclosure Delay
Bibliographic record
Abstract
Although many forms of victimization are repeated (e.g., domestic violence), we know relatively little about the perceived credibility of adult claimants who allege repeated maltreatment. We examined the effects of Event Frequency (Single vs. Repeated), Language Specificity (Episodic vs. Generic), and Disclosure Delay (Immediate vs. Delayed) on laypersons’ perceptions of claimant credibility. Participants ( N = 649) read a mock interview transcript and provided subjective ratings (e.g., credibility, likelihood of suspect guilt, claimant responsibility). When the alleged abuse occurred a single time (vs. repeatedly), participants rated the interviewee as less blameworthy but no more (or less) credible. Exploratory findings indicated that female participants viewed the interviewee as more credible and less responsible than did male participants.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".