Children’s endorsement of prosocial lies according to content and recipient knowledge
Bibliographic record
Abstract
Children and adults use prosocial lies in their everyday conversational exchanges. However, their use of this language form varies across contexts. Extending work demonstrating the importance of conversational partner knowledge for communicative decisions, we examine whether children (ages 8–11 years old; N = 81) and adults’ ( N = 218) endorsement of prosocial lies (and truths) differ based on whether a recipient is/is not knowledgeable of the context. Additionally, we examine whether such endorsements varied based on whether the lie (or truth) was about their opinion or the objective reality. Participants were asked to imagine themselves within a scenario with another person who was unaware/aware of a negative event. They then rated how likely they would be to use truth/lie statements which varied in content (i.e., reference to opinion or reality). While children endorsed statements similarly for ignorant/knowledgeable recipients, adults were more likely to endorse telling a prosocial lie when the recipient was ignorant of the negative event. Both groups indicated higher likelihood of telling a prosocial lie about an opinion versus reality. Addressing individual factors, self-reported empathy was not associated with children’s responses but was associated with adults’ communicative choices. Together this work provides information as to how children (and adults) use varying language forms to navigate social situations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".