MétaCan
Menu
Back to cohort
Record W2333328390 · doi:10.1177/0042098015576316

Open area and road density as land use indicators of young offender residential locations at the small-area level: A case study in Ontario, Canada

2015· article· en· W2333328390 on OpenAlexafffundabout
Jane Law, Matthew Quick, Ping W. Chan

Bibliographic record

VenueUrban Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLand useGeographyEnvironmental planningRural areaLand-use planningEnforcementResidential areaSocioeconomicsEnvironmental resource managementSociologyPolitical scienceCivil engineeringEnvironmental science

Abstract

fetched live from OpenAlex

This research explores associations between land use types and young offender residential location in the Regional Municipality of York, Ontario, Canada, at a small-area level. Employing a Bayesian spatial modelling approach, we found that after controlling for socio-economic risk factors, proportion of open area land use was positively associated, and road density negatively associated, with residential location of young offenders. Map decomposition, which visualises the contribution of each risk factor to total young offender risk, demonstrated that open area land use contributed more risk in rural areas than urban, and that road density contributed less risk in urban areas than rural. We propose explanations for these results focused on social disorganisation theory and accessibility to structured leisure activities and apply findings to inform law enforcement and land use planning. Results provide a criminological perspective not often considered in planning and urban studies research and contrast land use policies generally motivated by public health and the 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 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 score0.102
Threshold uncertainty score0.408

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.284
GPT teacher head0.378
Teacher spread0.094 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations14
Published2015
Admission routes3
Has abstractyes

Explore more

Same venueUrban StudiesSame topicCrime Patterns and InterventionsFrench-language works237,207