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Record W3209998351 · doi:10.1002/acp.3890

Using cognitive instructions to elicit narrative differences between children's true and false testimonies

2021· article· en· W3209998351 on OpenAlexafffund
Donia Tong, Joshua Wyman, Victoria Talwar

Bibliographic record

VenueApplied Cognitive Psychology · 2021
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsOntario Tech UniversityMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyCognitionInterviewCognitive interviewNarrativeDeceptionLie detectionSocial psychologyDevelopmental psychologyLinguisticsPsychiatryLaw

Abstract

fetched live from OpenAlex

Abstract There is a need to tell if children are providing truthful testimonies in legal cases. This study examined differences between children's true and false statements obtained using either an interview that included cognitive instructions or one that did not. Children witnessed a theft that they were asked to deny and were interviewed with or without cognitive instructions. Truth‐tellers were more forthcoming when they were interviewed using cognitive instructions. Honest statements contained more information about the theft than dishonest statements, but only amongst children interviewed using cognitive instructions. All cognitive instructions were effective at eliciting more details from truth‐tellers than liars. Overall, cognitive instructions encourage children to be forthcoming and provide informative testimonies, as well as facilitate lie detection. Findings can inform interviewing practices that incorporate cognitive instructions for forensic and legal professionals to use with child witnesses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.076
GPT teacher head0.389
Teacher spread0.313 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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