Exploring Parent–Child Discussions of Crime and their Influence on Children's Memory
Bibliographic record
Abstract
When children witness or experience criminal events, the first people they go to are generally parents. Typically, no one else is privy to these conversations, and consequently little is known about their specific content. Research has shown that children can be quite accurate witnesses at times. However, they can also incorporate information from misleading and suggestive questions into their recall, and once their event memory has been changed, children may be unable to provide accurate reports. It is important then to assess parent-child discussions about crime. In the present study, 7- to 10-year-old children watched a video of a theft and talked about it with a parent immediately afterwards, and half had a second immediate interview with a researcher. All were interviewed by a different interviewer 1 week later. Results showed that: parents relied on direct and yes/no questions; children made errors of commission in response to questions; some parents asked leading or misleading questions; children incorporated all correct information from leading questions and nearly 40% of incorrect information from misleading questions; children provided additional information when interviewed by an interviewer 1 week later; and children remained relatively accurate in their descriptions but some were more accurate with parents than with an interviewer 1 week later.
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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 | 0.003 | 0.015 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".