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Record W4292624158 · doi:10.3389/fpsyg.2022.982012

An experimental investigation of association between children’s lying and behavior problems

2022· article· en· W4292624158 on OpenAlexaff
Xue Liu, Siyuan Shang, Sarah Zanette, Yongkang Zhang, Qingzhou Sun, Liyang Sai

Bibliographic record

VenueFrontiers in Psychology · 2022
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of Regina
FundersHangzhou Normal UniversityNational Natural Science Foundation of China
KeywordsLyingPsychologyAggressionDevelopmental psychologyAssociation (psychology)Juvenile delinquencyCorrelationMedicine

Abstract

fetched live from OpenAlex

Children’s lying is a major concern for parents and teachers alike, not only because lying is an antisocial behavior but also because children’s lying correlates with other behavior problems, such as aggression and delinquency. Despite considerable correlational evidence demonstrating the relation between children’s lying and behavior problems, experimental evidence is scarce. This study uses a novel task to experimentally examine the relation between lying for personal reward and behavior problem symptoms among 9- to 11-year-old typically developed children (N = 275, 139 boys). Results revealed a positive correlation between children’s lying for personal reward and their behavior problem symptoms, and this correlation increases with age. Overall, this study provides experimental evidence suggesting children’s lying for personal reward is associated with behavior problems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.329
Teacher spread0.304 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations3
Published2022
Admission routes1
Has abstractyes

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