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Record W2175802668 · doi:10.1558/sll.2003.10.1.62

Earwitness identification over the telephone and in field settings

2003· article· en· W2175802668 on OpenAlexaff
A. Daniel Yarmey

Bibliographic record

VenueInternational Journal of Speech Language and the Law · 2003
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsIdentification (biology)PsychologyTest (biology)CommunicationSpeech recognitionVoice analysisField (mathematics)Computer scienceMathematics

Abstract

fetched live from OpenAlex

Earwitnesses were asked to describe and identify the voice of a young woman to whom they had spoken approximately five minutes earlier either in a naturalistic field setting or over the telephone. Witnesses were given a single tape-recorded voice of either the target or a highly similar foil, or a target present or a target absent six-person voice line-up. Half of the witnesses in the naturalistic settings were given a photograph of the target as a retrieval cue when they attempted to describe and identify the voice of the target. Witnesses gave few descriptions of the speaker’s voice. Voice identification was poor in both types of setting. Those witnesses who were prepared for a memory test were superior to non-prepared witnesses on the subsequent identification test. Photographic retrieval cues did not influence voice descriptions, but did minimize false identifications on the target absent line-up for witnesses prepared for the test. The six-person line-up proved to be significantly superior to the one-person lineup in minimizing false identifications of the most highly similar sounding foil.

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)0.002
metaresearch head score (Gemma)0.011
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.007
GPT teacher head0.308
Teacher spread0.301 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

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Same venueInternational Journal of Speech Language and the LawSame topicDeception detection and forensic psychologyFrench-language works237,207