Polite, instrumental, and dual liars
Bibliographic record
Abstract
Lying is an interpersonal exercise that requires the intentional creation of a false belief in another’s mind. As such, children’s development of lie-telling is related to their increasing understanding of others and may reflect the acquisition of basic social skills. Although certain types of lies may support social relationships, other types of lies are considered antisocial in nature. The goal of this study was to compare several possible correlates, such as cognitive ability and children’s behavior patterns, that may be associated with children’s ( N = 133) use of lies in socially acceptable versus socially unacceptable ways. Children engaged in two lie-telling paradigms: one to measure socially accepted (polite) lies and one to measure socially unaccepted (instrumental) lies. Results indicate that instrumental liars were young with low theory of mind (ToM) scores and had high social skills. Polite liars were the oldest, had high ToM, and had similar levels of social skills as instrumental liars. Truth-tellers and dual liars had lower social skills and moderate ToM in comparison to the instrumental and polite liars. These findings suggest that children use lies selectively to achieve their social goals, and also suggest that children’s lying behavior may change from being self-motivated to being other-motivated as they age, which may reflect socialization toward socially accepted 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".