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Record W4406939849 · doi:10.4135/9781483398204.n4

Let ’Em Talk!: A Field Study of Police Questioning Practices of Suspects and Accused Persons*

2016· book-chapter· en· W4406939849 on OpenAlexaboutno aff
Brent Snook, Kirk Luther, Heather Quinlan, Rebecca Milne

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)CriminologyPsychologyPolitical scienceMathematics

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 long- est 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 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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.709
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0140.000

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 head0.056
GPT teacher head0.373
Teacher spread0.317 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2016
Admission routes1
Has abstractyes

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