Promoting Honesty: The Influence of Stories on Children's Lie‐Telling Behaviours and Moral Understanding
Bibliographic record
Abstract
Moral stories are a means of communicating the consequences of our actions and emphasizing virtuous behaviour, such as honesty. However, the effect of these stories on children's lie‐telling has yet to be thoroughly explored. The current study investigated the influence of moral stories on children's willingness to lie for another individual. Children were read one of three stories prior to being questioned about an accidental wrongdoing: (1) a positive story, which emphasized the benefits of being honest; (2) a negative story, which outlined the potential costs of lying; and (3) a neutral story, which was unrelated to truth‐telling or lie‐telling. Initially, most children withheld information about the event. Older children were better able to maintain their lies throughout the interview. However, when asked direct questions, children in the positive story condition were more likely to tell the truth than those in the negative and neutral conditions. No significant differences were found between the negative and neutral story conditions. The present study also investigated the relationship between children's conceptual understanding and behaviour. The findings revealed that children's knowledge of truths and lies increased with age. Children who lied had significantly higher conceptual scores than those who did not lie. Furthermore, the type of story children were read had a significant impact on their evaluations of true and false statements. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.024 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".