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Record W2755100495 · doi:10.1177/0165025417728583

Practice makes perfect? The impact of coaching and moral stories on children’s lie-telling

2017· article· en· W2755100495 on OpenAlexaff
Victoria Talwar, Sarah Yachison, Karissa Leduc, Pooja Megha Nagar

Bibliographic record

VenueInternational Journal of Behavioral Development · 2017
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMcGill University
Fundersnot available
KeywordsCoachingPsychologyDevelopmental psychologySocial psychologyPsychotherapist

Abstract

fetched live from OpenAlex

Children ( n = 202; 4 to 7 years old) witnessed a confederate break a toy and were asked to keep the transgression a secret. Children were randomly assigned to a Coaching condition (i.e., No Coaching, Light Coaching, or Heavy Coaching) and a Moral Story condition (i.e., Positive or Neutral). Overall, 89.7% of children lied about the broken toy when asked open-ended questions about the event. During direct questions, children in the Heavy Coaching condition lied more than children in the No Coaching and Light Coaching conditions. Older children were influenced by both Heavy Coaching and Light Coaching, whereas younger children were influenced only by Heavy Coaching. Children in the Positive Story condition were less likely to maintain their lies than those in the Neutral Story condition. An interaction between Coaching and Moral Story conditions influenced lie-maintenance.

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)0.027
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.443
Teacher spread0.373 · 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

Citations24
Published2017
Admission routes1
Has abstractyes

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