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Record W2123961193 · doi:10.1177/0093854812449216

Let ’em Talk!

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

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

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.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.897
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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