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Record W2123461506 · doi:10.1177/0093854811412170

Strategic Sequences in Police Interviews and the Importance of Order and Cultural Fit

2011· article· en· W2123461506 on OpenAlexaff
K. Beune, Ellen Giebels, Wendi L. Adair, Bob M. Fennis, Karen I. van der Zee

Bibliographic record

VenueCriminal Justice and Behavior · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicSocial and Intergroup Psychology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsActive listeningContext (archaeology)PsychologyOrder (exchange)Social psychologyStrategic communicationPublic relationsPolitical scienceCommunicationHistoryEconomics

Abstract

fetched live from OpenAlex

This study introduces the concept of strategic sequences to police interviews and concentrates on the impact of active listening behavior and rational arguments. To test the authors’ central assumption that the effectiveness of strategic sequences is dependent on cultural fit (i.e., the match with the cultural background of suspects), young people participated in virtual police interviews. Study 1 demonstrated that contrast sequences accentuating rational rather than relational behavior were found to be effective in eliciting information and admissions from suspects originating from cultures that tend to use more direct and content-oriented communication (i.e., low-context cultures), whereas for suspects from cultures that use more indirect and context-oriented communication (i.e., high-context cultures) a nonsignificant trend in reversed order was found. Study 2 added the investigation of the joint impact of active listening and rational arguments. In line with predictions, the results showed that an active listening—rational arguments sequence is most effective when active listening behavior precedes— rather than follows—rational arguments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.118
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.221
GPT teacher head0.416
Teacher spread0.194 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations30
Published2011
Admission routes1
Has abstractyes

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