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Record W2157227386 · doi:10.1080/00207594.2012.746463

Instrumental lying by parents in the US and China

2012· article· en· W2157227386 on OpenAlexaff
Gail D. Heyman, Anna Shan Chun Hsu, Genyue Fu, Kang Lee

Bibliographic record

VenueInternational Journal of Psychology · 2012
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsLyingPsychologyChinaCompliance (psychology)FeelingDevelopmental psychologySocial psychologySociocultural evolutionMedicineLawPolitical science

Abstract

fetched live from OpenAlex

The practice of lying to one's children to encourage behavioral compliance was investigated among parents in the US (N = 114) and China (N = 85). The vast majority of parents (84% in the US and 98% in China) reported having lied to their children for this purpose. Within each country, the practice most frequently took the form of falsely threatening to leave a child alone in public if he or she refused to follow the parent. Crosscultural differences were seen: A larger proportion of the parents in China reported that they employed instrumental lie-telling to promote behavioral compliance, and a larger proportion approved of this practice, as compared to the parents in the US. This difference was not seen on measures relating to the practice of lying to promote positive feelings, or on measures relating to statements about fantasy characters such as the tooth fairy. Findings are discussed with reference to sociocultural values and certain parenting-related challenges that extend across cultures.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.179

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.001
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 head0.024
GPT teacher head0.391
Teacher spread0.367 · 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 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

Citations88
Published2012
Admission routes1
Has abstractyes

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