Verbalizing a commitment reduces cheating in young children
Bibliographic record
Abstract
Abstract Children are frequently given rules and permissions that contrast their self‐interest, resulting in cheating behavior. The present study examined whether a verbalized commitment without the word ‘promise’ could reduce cheating rates in young children and whether this technique would be significantly more effective than a simple affirmation to a request not to cheat. Ninety‐nine 3‐to‐5‐year‐olds were randomly assigned to one of three obligation conditions: control, simple ‘okay’, or a verbalized commitment condition. All children played a guessing game in which the experimenter left the room on the final trial and children were instructed not to peek at the toy in the experimenter's absence. Children were asked to agree to the request not to peek (simple ‘okay’ condition), to verbally state that they would not peek (verbalized commitment condition), or were just instructed not to peek (control condition). The verbalized commitment condition significantly reduced cheating rates compared to the other conditions, regardless of age. Furthermore, among those who cheated, children in the verbalized commitment condition took significantly longer to peek compared to the other conditions. Results suggest that a verbal commitment without the word ‘promise’ can be an effective method to reduce young children's cheating behavior.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".