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Record W2529803599 · doi:10.1016/j.jarmac.2016.07.005

Looking down the barrel of a gun: What do we know about the weapon focus effect?

2016· article· en· W2529803599 on OpenAlexaff
Jonathan M. Fawcett, Kristine A. Peace, Andrea Greve

Bibliographic record

VenueJournal of Applied Research in Memory and Cognition · 2016
Typearticle
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMacEwan University
FundersMedical Research Council
KeywordsWitnessFocus (optics)PsychologyEconomic JusticeCriminologySelection (genetic algorithm)Criminal justiceSocial psychologyComputer securityLawPolitical scienceComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Eyewitness memory for the perpetrator or circumstances of a crime is generally worse for scenarios involving weapons compared to those involving non-weapon objects—a pattern known for decades as the weapon focus effect. But despite ample support from laboratory experiments and recognition by experts, testimony concerning weapon focus is rarely admissible in court. The present article summarizes a selection of key findings within the weapon focus literature and considers whether the effect warrants consideration by the criminal justice system at this time. We conclude that weapon focus is sufficiently robust and uncontroversial to guide practice so long as consideration is given to the circumstances surrounding the criminal event with a particular emphasis on witness expectation.

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)0.011
metaresearch head score (Gemma)0.071
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.071
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.005
Scholarly communication0.0030.008
Open science0.0030.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0100.001

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 head0.039
GPT teacher head0.373
Teacher spread0.334 · 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

Citations16
Published2016
Admission routes1
Has abstractyes

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