Children's Lie-Telling to Conceal a Parent's Transgression: Legal Implications.
Bibliographic record
Abstract
Children's lie-telling behavior to conceal the transgression of a parent was examined in 2 experiments. In Experiment 1 (N = 137), parents broke a puppet and told their children (3-11-year-olds) not to tell anyone. Children answered questions about the event. Children's moral understanding of truth- and lie-telling was assessed by a second interviewer and the children then promised to tell the truth (simulating court competence examination procedures). Children were again questioned about what happened to the puppet. Regardless of whether the interview was conducted with their parent absent or present, most children told the truth about their parents' transgression. When the likelihood of the child being blamed for the transgression was reduced, significantly more children lied. There was a significant, yet limited, relation between children's lie-telling behavior and their moral understanding of lie- or truth-telling. Further, after children were questioned about issues concerning truth- and lie-telling and asked to promise to tell the truth, significantly more children told the truth about their parents' transgression. Experiment 2 (N = 64) replicated these findings, with children who were questioned about lies and who then promised to tell the 'truth more likely to tell the truth in a second interview than children who did not participate in this procedure before questioning. Implications for the justice system are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".