Sizing up Crime and Weather Relationships in a Small Northern City
Bibliographic record
Abstract
Small study areas are vastly underrepresented in the criminological literature, including the literature on the relationship between crime and weather. North Bay, ON (population 50,000) provides a useful study area in which to begin to address this lack. Using five years of police call for service data (2015–2019), negative binomial regression models were used to assess the relationships between weather variables and assaults, break and enters, domestic disputes, and thefts. For each crime type, the resulting models were compared based on their Aikake information criteria (AICs) to determine which performed the best. Significant relationships were found to differ between crime types. Temperature played a significant role in determining the temporal distribution of thefts, while for break and enters a model without weather variables performed best, even though both are property crimes. Similarly, for violent crimes, assaults were found to be positively correlated to temperature, while domestic disputes depended mainly on day of the week.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".