Longitudinal associations between lie evaluations and frequency: The moderating role of age
Bibliographic record
Abstract
Abstract While previous studies have demonstrated correlations between children and adolescents’ evaluations of lies and lie‐telling behaviors, the temporal order of these associations over time and changes across this developmental period remain unexamined. The current study examined longitudinal associations among children and adolescents’ ( N = 1128; M age = 11.54, SD = 1.68, 49.80% male, and 83.6% white) evaluations of lies to parents for autonomy and lie‐telling frequency to parents and friends. Autoregressive cross‐lagged analysis revealed longitudinal associations moderated by age. Among children, evaluations of lies predicted greater lie‐telling rates over time. Conversely, among adolescents, lie‐telling frequency predicted lie evaluations over time, and evaluations predicted lying to parents over time. These results demonstrate a novel developmental pattern of the associations between moral evaluations of lies and lie‐telling. Research Highlights Children and adolescents’ evaluations of lie‐telling and lie‐telling frequency were associated longitudinally, but the direction of this association was moderated by age. Among children, more positive lie evaluations predicted greater lie‐telling to parents and friends over time. Among adolescents, more positive lie evaluations predicted lying more often to parents over time; lying more to parents and friends predicted more positive evaluations over time. These findings suggest a novel developmental pattern regarding the temporal order of the association between evaluations of lie‐telling and lie‐telling frequency.
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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 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 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".