Assessing children's competency to take the oath in court: The influence of question type on children's accuracy.
Bibliographic record
Abstract
This study examined children's accuracy in response to truth-lie competency questions asked in court. The participants included 164 child witnesses in criminal child sexual abuse cases tried in Los Angeles County over a 5-year period (1997-2001) and 154 child witnesses quoted in the U.S. state and federal appellate cases over a 35-year period (1974-2008). The results revealed that judges virtually never found children incompetent to testify, but children exhibited substantial variability in their performance based on question-type. Definition questions, about the meaning of the truth and lies, were the most difficult largely due to errors in response to "Do you know" questions. Questions about the consequences of lying were more difficult than questions evaluating the morality of lying. Children exhibited high rates of error in response to questions about whether they had ever told a lie. Attorneys rarely asked children hypothetical questions in a form that has been found to facilitate performance. Defense attorneys asked a higher proportion of the more difficult question types than prosecutors. The findings suggest that children's truth-lie competency is underestimated by courtroom questioning and support growing doubts about the utility of the competency requirements.
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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 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".