Children’s Evaluations of Tattles, Confessions, Prosocial and Antisocial Lies
Bibliographic record
Abstract
Lie-telling is a false verbal statement made with the intention to deceive another. Lies may be told for selfish reasons or due to prosocial motivations. As a result, the veracity of a statement holds more than just communicative intent but rather represents social intentions. In the current experiment children (6- to 12-years old) viewed 12 vignettes which depicted a protagonist either telling a truth or a lie. The protagonist’s statements either hurt another or themselves (other versus self). Following viewing of each vignette participants provided a moral evaluation of the protagonist’s statement (five-point Likert) and a classification of the statements; as either a truth or lie. Additionally, a novel method of evaluating statements was introduced, whereby children evaluated communicative intent as an act, to be rewarded or punished. Results revealed that both lies and truths were accurately identified, with the exception of altruistic lies (benefits to another) and tattling truths (harms another). Younger children rewarded truthful statements, which harmed or hurt another, significantly more often than older children. Older children ranked lies to help another significantly more favorably than lies to protect the self. Children also rewarded confessions and punished antisocial lies most frequently. Results highlight the notable differences in children’s perceptions of varying forms of honesty and lying.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".