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Record W2793350323 · doi:10.1002/bsl.2331

Does parental coaching affect children's false reports? Comparing verbal markers of deception

2018· article· en· W2793350323 on OpenAlexaff
Victoria Talwar, Kyle Hubbard, Christine Saykaly, Kang Lee, R. C. L. Lindsay, Nicholas Bala

Bibliographic record

VenueBehavioral Sciences & the Law · 2018
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of TorontoQueen's UniversityMcGill University
Fundersnot available
KeywordsDeceptionCoachingAffect (linguistics)PsychologyNarrativeSession (web analytics)Developmental psychologyCognitionSocial psychologyPsychiatryCommunicationPsychotherapistAdvertising

Abstract

fetched live from OpenAlex

The present study examined differences in children's true and false narratives as a function of parental coaching by comparing the verbal markers associated with deception. Children (N = 65, 4-7 years old) played the same game with an adult stranger over three consecutive days. Parents coached their children to falsely allege that they had played a second game and to generate details for the fabricated event. One week after the last play session, children were interviewed about their experiences. For children with the least amount of parental coaching, true and false reports could be distinguished by multiple verbal markers of deception (e.g., cognitive processes, temporal information, self-references). The fabricated reports of children who spent more time being coaching by a parent resembled their truthful reports. These findings have implications for real-world forensic contexts when children have been coached to make false allegations and fabricate information at the behest of a parent.

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)0.003
metaresearch head score (Gemma)0.035
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 score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.035
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.047
GPT teacher head0.372
Teacher spread0.325 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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