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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 OpenAlex
Jonathan M. Fawcett, Kristine A. Peace, Andrea Greve

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score1.000

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.039
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