MétaCan
Menu
Back to cohort

Crime Pattern Theory

2021· reference-entry· en· W2890106829 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

VenueOxford Research Encyclopedia of Criminology and Criminal Justice · 2021
Typereference-entry
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCriminologyEveryday lifeCrime preventionSpace (punctuation)PopulationGeographyHuman settlementSocial spaceSocioeconomic statusSociologyPolitical scienceComputer scienceDemographyLaw

Abstract

fetched live from OpenAlex

Abstract A broad understanding of crime requires explanations for both the origins of individual and group criminal propensity and when and where criminal events occur. Crime pattern theory provides explanations for the variation in the distribution of criminal events in space and time given a range of different propensities. In the organization of their everyday lives, both occasional and persistent criminals spend most of their time engaged in the same legitimate everyday activities as everyone else. The location of criminal events in space–time are shaped by these everyday activities and the specific criminal’s activity. Occasional and persistent offenders develop activity spaces and awareness spaces. The shape and dynamics of these spaces is influenced by the structures of human settlements that channel and limit movement patterns in time and space. These structures include the built environments and the socioeconomic and cultural environments in which people live, work, or go to school, and in which they spend their social, entertainment, and shopping time. Crime pattern theory utilizes the major components of the built and social environment—activity nodes, paths between nodes, neighborhoods and neighborhood edges, and the socioeconomic backcloth—in conjunction with the routine movements of the population in general to understand crime generator and crime attractor locations and the formation of repeat areas of offending for individuals and groups of offenders as well as more aggregate crime hot spots and cold spots. This information is translated into a geometry of crime that describes the journeys to crime by individual criminal offenders and groups of offenders and their victims or targets. Crime pattern theory explains the process of criminal target search, suggests strategies for crime reduction, and describes potential displacements of criminal events in space and time following changes in the suitability of targets or target locations at particular places and specific times.

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)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.779
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.000

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 head0.165
GPT teacher head0.430
Teacher spread0.265 · 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