Cheating in childhood: Exploring the link between parental reports of problem behaviors and dishonesty on simulated academic tests
Bibliographic record
Abstract
This study investigated the relationship between parental reports of children's behavioral problems and their cheating behaviors on simulated academic tests, addressing a significant gap in understanding early childhood academic cheating and its potential links to broader behavioral issues. We hypothesized that children's early problem behaviors would be predictive of their academic cheating. To test these hypotheses, children aged 4 to 12 years took part in six unmonitored academic tests that measured their cheating behaviors while their parents completed the Child Behavior Checklist and the Strengths and Difficulties Questionnaire elsewhere. Separate hierarchical linear regressions revealed that children's problem behaviors, as reported by parents, overall significantly predict children's cheating behaviors even after accounting for demographic variables such as age, gender, ethnicity, and parental religiosity. Specifically, the Conduct Problems subscale of the Strengths and Difficulties Questionnaire showed a significant and unique association with children's cheating behaviors above and beyond the common contributions of all predictors. However, the Child Behavior Checklist scores and the scores on the other Strengths and Difficulties subscales were not significantly or uniquely related to cheating. These findings offer new insight into simulated childhood academic cheating and its relation to problem behaviors observed by parents.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".