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Record W4205336085 · doi:10.1080/17445647.2021.2004940

Spatial distribution of criminal events in Lithuania in 2015–2019

2021· article· en· W4205336085 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

VenueJournal of Maps · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsVancouver Island University
Fundersnot available
KeywordsScale (ratio)Distribution (mathematics)Spatial distributionSpatial ecologyProperty crimeProperty (philosophy)

Abstract

fetched live from OpenAlex

The presented map poster represents statistics for 3.46 million events reported to the police in Lithuania in 2015–2019. For eight types of events (violent crime, theft, property crime, economic crime, infringement of public policy, drug-related crime, traffic accidents, various other events), two maps at a scale of 1:3,000,000 are presented. They demonstrate the values of location quotient and the main insights into the dynamics of crime over the five-year timeframe covered by the project. Two maps at scale 1:2,000,000 show the distribution of five types of events that are directly related to the safety of persons – totalling 1.67 million records. One of the larger scale maps depicts the relative crime rate, separately for densely and sparsely populated areas. The second map shows the relative crime risk surface. The maps enable a visual analysis of the most problematic areas in Lithuania and can enable deeper further investigation.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.608

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.037
GPT teacher head0.367
Teacher spread0.330 · 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