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Record W4226059525 · doi:10.1080/10888691.2022.2058507

The role of rapport in eliciting children’s truthful reports

2022· article· en· W4226059525 on OpenAlexafffund
Ida Foster, Victoria Talwar, Angela M. Crossman

Bibliographic record

VenueApplied Developmental Science · 2022
Typearticle
Languageen
FieldNeuroscience
TopicMemory Processes and Influences
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsInterviewPsychologyNarrativeSocial psychologyDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

Children (N = 114, ages 7–13) witnessed a transgressor steal money from a wallet and then asked them to lie about the theft when interviewed by a novel interviewer. During the interview, children were asked to either describe various experienced events (Narrative Practice Rapport-building condition) or participate in an interactive activity designed to focus on the relational aspects of rapport-building including mutual attentiveness, positivity, and coordination between child and interviewer (Interactional Rapport-building condition). Children also completed a measure of rapport to indicate their subjective level of rapport with the interviewer. Older children in the Interactional Rapport-building condition were significantly more likely to be truthful, disclose the transgression earlier, and give more details. Findings provide an initial, exploratory understanding of how the rapport-building phase in eyewitness interviews may play an important role in children’s disclosure decision-making and may be another area to study to promote more truthful disclosures.

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)0.022
metaresearch head score (Gemma)0.105
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.119

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.105
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0010.004
Research integrity0.0010.002
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 head0.014
GPT teacher head0.231
Teacher spread0.217 · 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
Published2022
Admission routes2
Has abstractyes

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