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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 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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.533
Threshold uncertainty score0.606

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.007
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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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