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Record W2134018870 · doi:10.1177/0093854811435208

The Violent Crime Linkage Analysis System

2012· article· en· W2134018870 on OpenAlex

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

VenueCriminal Justice and Behavior · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsCarleton UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsLinkage (software)Inter-rater reliabilityOfficerPsychologyReliability (semiconductor)MisconductEngineeringComputer securityComputer sciencePolitical scienceLawDevelopmental psychology

Abstract

fetched live from OpenAlex

The interrater reliability of an internationally renowned crime linkage system—the Violent Crime Linkage Analysis System (ViCLAS)—was tested. Police officers ( N = 10) were presented with a case file and asked to complete a ViCLAS booklet. The level of occurrence agreement between each officer was calculated. Results showed a 30.77% level of agreement across the 106 variables examined. Agreement ranged from 2.36% for weapon variables to 62.87% for administration variables. Only 11 (10.38%) of the variables reached an acceptable level of agreement. Concerns pertaining to the validity of inferences produced using ViCLAS data are discussed, along with potential explanations for the findings, limitations of the study, and future research directions.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.544
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.083
GPT teacher head0.399
Teacher spread0.316 · 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