The Development of Antisocial and Prosocial Lying in Children with and without Severe Conduct Problems
Bibliographic record
Abstract
Lying is a typical part of childhood. However, for some children, such as those experiencing conduct problems, lying becomes problematic and atypical with increased age. Frequent and persistent antisocial lying is thought to be an early symptom of conduct problems. However, until the present thesis, it was unclear why children with conduct problems tell antisocial lies more than typical children. Furthermore, it was unknown whether and how often children with conduct problems tell prosocial lies (i.e., “white lies”), which are lies told to be polite or prevent hurting someone else’s feelings. This thesis outlines 3 studies (N = 316-387) examining antisocial and prosocial lie-telling among 6 to 12 year-old children with and without conduct problems. In Chapter 2, I found that while parents reported that the frequency of children’s antisocial lying increased alongside the severity of conduct problems, the frequency of their prosocial lying did not. Given that parent report methods may be unreliable due to social desirability bias or the inherent nature of lying being deceptive (i.e., parents may not catch their child lying), in Chapters 3 and 4 I examined prosocial and antisocial lying using behavioral measures. In Chapter 3, I used a disappointing gift paradigm to examine whether children would lie about liking a disappointing gift. Consistent with the parent-report results of Chapter 2, children with conduct problems were just as likely to tell a prosocial lie as typically developing children. In Chapter 4, I used a modified version of the temptation resistance paradigm to examine whether children will tell a lie to conceal a minor transgression (a form of antisocial lying). Again, consistent with the parent-report results of Chapter 2, children were more likely to tell an antisocial lie with increased conduct problems. However, theory of mind was shown to act as a protective factor against the relationship between increased antisocial lying and conduct problems. Together, my thesis suggests that children with and without conduct problems engage in different types of lying according to social context, and that socio-cognitive development may play a key role in the reduction of antisocial lying for children with conduct problems.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".