An Experimental Investigation of Antisocial Lie-Telling Among Children With Disruptive Behavior Disorders and Typically Developing Children
Bibliographic record
Abstract
Children's lie-telling is surprisingly understudied among children with significant behavioral problems. In the present study, experimental paradigms were used to examine antisocial lie-telling among ethnically diverse 5- to 10-year-old children with disruptive behavior disorders (DBD; n = 71) and a typically developing (TD) comparison sample (n = 50) recruited from a southeastern state from 2013 to 2014. Children completed two games that measured the prevalence and skill of their lies: (a) for personal gain and (b) to conceal wrongdoing. Children with DBD were more likely to lie for personal gain than TD children. With age, children were more likely to lie to conceal wrongdoing, but the reverse was true regarding lies for personal gain. Results advance knowledge concerning individual differences in children's lie-telling.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".