Children’s persistent lying, gender differences, and disruptive behaviours: A longitudinal perspective
Bibliographic record
Abstract
Background: Although lying by children can be a serious problem for parents and educators, there are hardly any longitudinal data to help understand its development. The aim of this study was to understand the stability of consistent lying from 6 to 8 years of age, and its association with other behaviour problems rated concurrently and subsequently. Methods: Teachers and mothers rated lying and disruptive behaviours of a population sample of boys ( N = 549) and girls ( N = 579) for three consecutive years when the children were 6, 7, and 8. Teachers also rated children’s disruptive behaviours at ages 10 and 11. Results: Mothers tended to rate their children as lying more often than teachers did. Girls were rated by both adults and across ages, as lying less than boys. The number of consistent liars (i.e., lying according to both adults at a given age) was the same at different ages. Persistent liars (consistent liars from ages 6 to 8) were rated more disruptive concurrently and subsequently by teachers. However, their disruptive behaviours did not increase from ages 6, 7, and 8 through ages 10 and 11 compared to other groups. Conclusions: Lying is common for 6- to 8-year-old children, but more frequent for males. Frequent lying, as reported by mothers and teachers, appears to become persistent by 7 years of age. Persistent lying was concurrently associated to disruptive behaviour problems. Some patterns of lying were also predictive of increases in disruptive behaviour but this finding was inconsistent and suggested the need for further research. Future studies need also to focus on the content of the lies, and their timing, to understand their function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".