Lie-telling for personal gain in children with and without externalizing behavior problems
Bibliographic record
Abstract
Few studies have examined the lie-telling behavior of children who have externalizing problems using experimental procedures. In the current study, children's lie-telling for personal gain (N = 110 boys aged 6-11 years) was examined using an experimental paradigm in relation to their theory-of-mind abilities and inhibitory control as well as their moral evaluations of truths and lies. Children with externalizing behavior problems (n = 53) were significantly more likely to lie and to be less skilled at lying than a typical comparison group (n = 57). Children who had lower theory-of-mind scores were significantly more likely to tell a lie for personal gain compared with those who had higher theory-of-mind scores. Children with externalizing problems who told personal gain lies were also more likely to rate tattle truths more positively than other children. For a subsample of children (n = 55), parent-reported diaries of the frequency of children's lies over 2 weeks revealed a higher frequency of lies by children with externalizing problems compared with the typical comparison group. Children whose parents reported a high frequency of lies for their children were also more likely to lie in the experimental personal gain lie paradigm. Results suggest that children with externalizing behavior may have a different pattern of lie-telling than has been previously reported for normative lie development.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".