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Record W2024044987 · doi:10.1002/acp.1481

Toward a more informative psychological science of eyewitness evidence

2008· article· en· W2024044987 on OpenAlexaff
John W. Turtle, J. Don Read, D. Stephen Lindsay, C. A. Elizabeth Brimacombe

Bibliographic record

VenueApplied Cognitive Psychology · 2008
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsUniversity of VictoriaSimon Fraser UniversityToronto Metropolitan University
Fundersnot available
KeywordsPsychologyPsychological scienceLaw enforcementEyewitness testimonyForensic psychologyPsychological researchEmpirical evidenceEmpirical researchLegal psychologySocial psychologyCriminologyLawEpistemologyPolitical science

Abstract

fetched live from OpenAlex

Abstract Like Hugo Münsterberg, we believe that psychological science can inform the courts and police regarding eyewitness evidence. But 100 years into the enterprise, the body of knowledge acquired to date demands considerable circumspection, both in the claims expert psychological witnesses make in court and in the recommendations psychologists tender to investigating officers. There are a number of points regarding eyewitness evidence that psychologists can offer with considerable confidence, but many matters are as yet open to debate (and some issues are likely to remain unsettled for a long time). We encourage researchers, law enforcement and the legal community to (a) identify and prioritize the problems to be solved, (b) focus on a more integrative empirical approach, including more of the field experiments currently in use, as well as new descriptive research, especially on base rates and (c) use basic psychological theory and principles to consolidate the wide range of phenomena present in individual cases. Copyright © 2008 John Wiley & Sons, Ltd.

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 categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.008
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.003

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 head0.138
GPT teacher head0.436
Teacher spread0.298 · 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; both teacher heads agree on what is shown here.

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

Citations19
Published2008
Admission routes1
Has abstractyes

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