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Record W1997763964 · doi:10.1037/lhb0000077

Language style matching and police interrogation outcomes.

2014· article· en· W1997763964 on OpenAlexaff
Beth H. Richardson, Paul Taylor, Brent Snook, Stacey M. Conchie, Craig Bennell

Bibliographic record

VenueLaw and Human Behavior · 2014
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsCarleton UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsConfession (law)InterrogationMatching (statistics)PsychologySuspectStyle (visual arts)UtteranceSocial psychologyLinguisticsCriminologyLawPolitical science

Abstract

fetched live from OpenAlex

This research examined the coordination of interrogator and suspects' verbal behavior in interrogations. Sixty-four police interrogations were examined at the aggregate and utterance level using a measure of verbal mimicry known as Language Style Matching. Analyses revealed an interaction between confession and the direction of language matching. Interrogations containing a confession were characterized by higher rates of the suspect matching the interrogators' language style than interrogations without a confession. A sequence analysis of utterance-level Language Style Matching revealed a divergence in the type of matching that occurred across outcome. There was a linear increase in interrogator-led matching for interrogations containing a confession and an increase in suspect-led matching for nonconfession interrogations. These findings suggest that police interrogations play out, in part, at the basic level of language coordination.

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)0.004
metaresearch head score (Gemma)0.058
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 score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.058
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.021
GPT teacher head0.351
Teacher spread0.330 · 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

Citations63
Published2014
Admission routes1
Has abstractyes

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