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Record W4400536312 · doi:10.1016/j.jecp.2024.105999

Relations among lie-telling self-efficacy, moral disengagement, and willingness to tell antisocial lies among children and adolescents

2024· article· en· W4400536312 on OpenAlexafffund
Donia Tong, İpek Işık, Victoria Talwar

Bibliographic record

VenueJournal of Experimental Child Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyMoral disengagementDevelopmental psychologySocial psychologySocial cognitive theoryPsychological interventionMoral developmentDisengagement theoryPsychosocialDisgustPsychotherapistAnger

Abstract

fetched live from OpenAlex

This study examined a proposed model of relations among lie-telling self-efficacy, moral disengagement, and willingness to tell antisocial lies among children and adolescents. Children and adolescents aged 6 to 15 years completed measures of lie-telling self-efficacy and moral disengagement. They also read vignettes about a character committing a transgression and telling a lie to conceal the transgression. For each vignette, children and adolescents made a hypothetical decision about telling the truth or a lie if they were in the character's position to assess their lie-telling propensity. Lie-telling self-efficacy was related to willingness to tell lies, and this relationship was mediated by moral disengagement. Children and adolescents with higher lie-telling self-efficacy had higher moral disengagement, and those who had higher moral disengagement were more willing to tell antisocial lies. Overall, results support Bandura's social cognitive theory as a framework for understanding the psychosocial mechanisms underlying attitudes toward lie-telling. Moreover, these findings suggest that interventions to address problematic lie-telling behavior should focus on children's and adolescents' use of moral disengagement mechanisms.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.333
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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