U.S. Defense Attorneys’ Implicit Questioning of Children in Child Sexual Assault Trials
Bibliographic record
Abstract
= 122) of children in child sexual assault trials and analyzed how often they rebutted these questions. Through qualitative content analysis, we found that defense attorneys most commonly asked children implicit questions about: ulterior motives, coaching, being untruthful, missing disclosure opportunities, having poor memory, and other credibility issues. Implicit questions were posed in 63% of cases, with children rebutting only 11% of implied inquiries. We observed no significant correlations between the age of children testifying and the overall frequency of implicit questions or rebuttals. However, age differences were found based on the content of the questions; younger children (aged 6-12) were more frequently subjected to implicit inquiries about coaching, whereas teenaged adolescents (aged 13-17) faced more questions related to truthfulness and credibility issues. In conclusion, children were frequently asked implicit questions that implied credibility concerns, which may be difficult for children to understand. Furthermore, defense attorneys change the focus of the content of their implicit questions depending on the age of the child testifying.
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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 | 0.005 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".