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Record W2123092086

Police Interviewing and Interrogation: A Self-Report Survey of Police Practices and Beliefs

2007· article· en· W2123092086 on OpenAlexaff
Richard A. Leo, Saul M. Kassin, Christian A. Meissner, Kimberly D. Richman, Lori H. Colwell, Amy Leach, Dana La Fon

Bibliographic record

VenueSSRN Electronic Journal · 2007
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsQueen's University
Fundersnot available
KeywordsInterrogationInterviewAppealPsychologyConsistency (knowledge bases)Social psychologyCriminologyPolitical scienceLawComputer science
DOInot available

Abstract

fetched live from OpenAlex

By questionnaire, 631 police investigators reported on their interrogation beliefs and practices - the first such survey ever conducted. Overall, participants estimated that they were 77 percent accurate at truth and lie detection, that 81 percent of suspects waive Miranda rights, that the mean length of interrogation is 1.6 hours, and that they elicit self-incriminating statements from 68 percent of suspects, 4.78 percent from innocents. Overall, 81 percent felt that interrogations should be recorded. As for self-reported usage of various interrogation tactics, the most common were to physically isolate suspects, identify contradictions in suspects' accounts, establish rapport, confront suspects with evidence of their guilt, and appeal to self-interests. Results were discussed for their consistency with prior research, policy implications, and methodological shortcomings.

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)0.006
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.557
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
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 integrity0.0000.001
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 head0.042
GPT teacher head0.386
Teacher spread0.345 · 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 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

Citations47
Published2007
Admission routes1
Has abstractyes

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