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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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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