Complex Questions Asked by Defense Lawyers but Not Prosecutors Predicts Convictions in Child Abuse Trials
Bibliographic record
Abstract
Attorneys’ language has been found to influence the accuracy of a child’s testimony, with defense attorneys asking more complex questions than the prosecution (Zajac & Hayne, J. Exp Psychol Appl 9:187–195, 2003; Zajac et al. Psychiatr Psychol Law, 10:199–209, 2003). These complex questions may be used as a strategy to influence the jury’s perceived accuracy of child witnesses. However, we currently do not know whether the complexity of attorney’s questions predict the trial outcome. The present study assesses whether the complexity of questions is related to the trial outcome in 46 child sexual abuse court transcripts using an automated linguistic analysis. Based on the complexity of defense attorney’s questions, the trial verdict was accurately predicted 82.6% of the time. Contrary to our prediction, more complex questions asked by the defense were associated with convictions, not acquittals.
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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 | 0.003 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".