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Record W2123961193 · doi:10.1177/0093854812449216

Let ’em Talk!

2012· article· en· W2123961193 on OpenAlexaffabout
Brent Snook, Kirk Luther, Heather Quinlan, Rebecca Milne

Bibliographic record

VenueCriminal Justice and Behavior · 2012
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsInterviewNarrativePsychologySocial psychologyClosed-ended questionApplied psychologyPolitical scienceLawLinguistics

Abstract

fetched live from OpenAlex

The real-life questioning practices of Canadian police officers were examined. Specifically, 80 transcripts of police interviews with suspects and accused persons were coded for the type of questions asked, the length of interviewee response to each question, the proportion of words spoken by interviewer(s) and interviewee, and whether or not a free narrative was requested. Results showed that, on average, less than 1% of the questions asked in an interview were open-ended, and that closed yes–no and probing questions composed approximately 40% and 30% of the questions asked, respectively. The longest interviewee responses were obtained from open-ended questions, followed by multiple and probing question types. A free narrative was requested in approximately 14% of the interviews. The 80–20 talking rule was violated in every interview. The implications of these findings for reforming investigative interviewing of suspects and accused persons are discussed.

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)0.008
metaresearch head score (Gemma)0.027
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 score0.250
Threshold uncertainty score0.498

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.005

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 head0.077
GPT teacher head0.379
Teacher spread0.302 · 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

Citations81
Published2012
Admission routes2
Has abstractyes

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