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Record W1990840453 · doi:10.1080/1068316x.2011.599325

Of guns and geese: a meta-analytic review of the ‘weapon focus’ literature

2011· review· en· W1990840453 on OpenAlexaffabout
Jonathan M. Fawcett, Emily J. Russell, Kristine A. Peace, John Christie

Bibliographic record

VenuePsychology Crime and Law · 2011
Typereview
Languageen
FieldPsychology
TopicDeception detection and forensic psychology
Canadian institutionsMacEwan UniversityLakehead UniversityDalhousie University
Fundersnot available
KeywordsPsychologyScholarshipSchema (genetic algorithms)Social psychologyEvent (particle physics)Applied psychologyComputer scienceLawPolitical science

Abstract

fetched live from OpenAlex

Weapon focus is frequently cited as a factor in eyewitness testimony, and is broadly defined as a weapon-related decrease in performance on subsequent tests of memory for those elements of an event or visual scene concurrent to the weapon. This effect has been attributed to either (a) physiological or emotional arousal that narrows the attentional beam (arousal/threat hypothesis), or (b) the cognitive demands inherent in processing an unusual object (e.g. weapon) that is incongruent with the schema representing the visual scene (unusual item hypothesis). Meta-analytical techniques were applied to test these theories as well as to evaluate the prospect of weapon focus in real-world criminal investigations. Our findings indicated an effect of weapon presence overall (g= 0.53) that was significantly influenced by retention interval, exposure duration, and threat but unaffected by whether the event occurred in a laboratory, simulation, or real-world environment.

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)0.027
metaresearch head score (Gemma)0.098
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.098
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0070.012
Bibliometrics0.0120.011
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.160
GPT teacher head0.435
Teacher spread0.275 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations146
Published2011
Admission routes2
Has abstractyes

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