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 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 | 0.007 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".