High consistency of cheating and honesty in early childhood
Bibliographic record
Abstract
Three preregistered studies examined whether 5-year-old children cheat consistently or remain honest across multiple math tests. We observed high consistency in both honesty and cheating. All children who cheated on the first test continued cheating on subsequent tests, with shorter cheating latencies over time. In contrast, 77% of initially honest children maintained honesty despite repeated failure to complete the tests successfully. A brief integrity intervention helped initially honest children remain honest but failed to dissuade initially cheating children from cheating. These findings demonstrate that cheating emerges early and persists strongly in young children, underscoring the importance of early prevention efforts. They also suggest that bolstering honesty from the start may be more effective than attempting to remedy cheating after it has occurred. RESEARCH HIGHLIGHTS: Our research examines whether 5-year-old children, once they have started cheating, will continue to do so consistently. We also investigate whether 5-year-old children who are initially honest will continue to be honest subsequently. We discovered high consistency in both honesty and cheating among 5-year-old children. Almost all the children who initially cheated continued this behavior, while those who were honest stayed honest. A brief integrity-boosting intervention successfully helped 5-year-old children maintain their honesty. However, the same intervention failed to deter cheaters from cheating again. These findings underscore the importance of implementing integrity intervention as early as possible, potentially before children have had their first experience of cheating.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".