Crime Seasonality across Multiple Jurisdictions in British Columbia, Canada
Bibliographic record
Abstract
Seasonal changes in crime have been documented since the mid-1800s, but no definitive consensus has been reached regarding universal annual patterns. Researchers also tend to focus on a single city over a particular time period, and, due to methodological differences, studies can often be difficult to compare. As such, this study investigates the seasonal fluctuations of crime across eight cities in British Columbia, Canada, between 2000 and 2006. Uniform Crime Report data, representing four crime types (assault, robbery, motor vehicle theft, and break and enter) were used in negative binomial or Poisson count models and regressed against trend, weather, and illumination variables. Results suggest that temperature changes impacted assault levels, few weather variables affected the occurrence of robberies, and fluctuations in property crime types were variable across the cities. Moreover, rain and snow had a deterrent effect on crime in cities that were not used to such weather conditions. These findings imply that (a) changes in weather patterns modify peoples’ routine activities and, in turn, influence when crime is committed; (b) universal crime seasonality patterns should not be assumed across all cities; and (c) crime seasonality should be studied at a disaggregate or crime-specific level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".