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Record W2305291328 · doi:10.1177/0306624x16632259

Crime Seasonality: Examining the Temporal Fluctuations of Property Crime in Cities With Varying Climates

2016· article· en· W2305291328 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Offender Therapy and Comparative Criminology · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsProperty crimeOrdinary least squaresNegative binomial distributionGeographySeasonalityProperty (philosophy)EconometricsViolent crimeCriminologyStatisticsMathematicsSociology

Abstract

fetched live from OpenAlex

This study investigates whether crime patterns fluctuate periodically throughout the year using data containing different property crime types in two Canadian cities with differing climates. Using police report data, a series of ordinary least squares (OLS; Vancouver, British Columbia) and negative binomial (Ottawa, Ontario) regressions were employed to examine the corresponding temporal patterns of property crime in Vancouver (2003-2013) and Ottawa (2006-2008). Moreover, both aggregate and disaggregate models were run to examine whether different weather and temporal variables had a distinctive impact on particular offences. Overall, results suggest that cities that experience greater variations in weather throughout the year have more distinct increases of property offences in the summer months and that different climate variables affect certain crime types, thus advocating for disaggregate analysis in the future.

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)0.000
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 score0.442
Threshold uncertainty score0.492

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.447
GPT teacher head0.407
Teacher spread0.041 · 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