Assessing the impact of economic and demographic change on property crime rates in Western Canada
Bibliographic record
Abstract
Western provinces have experienced tremendous change over the last few decades, with oil booms and busts, with large international and interprovincial movement of workers and families, and with rising and declining property crime rates. What are the links between these economic, demographic, and crime rate changes? I investigate these links for Western Canada over the period from 1968 to 2015. Empirical results suggest that increases in household incomes and alcohol sales per capita, and decreases in unemployment rates, all signs of improved economic prosperity, coincided with decreases in rates of property crime. Increases in migration turnover (both inward and outward migration) put upward pressure on rates of property crime. In addition, changes in police reporting methods and categorization have had dramatic effects on official rates of property crime: the change from UCR1 to UCR2 reporting methodology caused rates of property crime to rise by between 18 per cent and 30 per cent in 1998, and changes in police reporting methods in 2003 caused property crime rates to rise again, by between 5 per cent and 16 per cent in 2003, for the western provinces. The recent rise in rates of property crime in the west is closely linked to the economic slowdown following the drop in oil and resource prices, and should migration turnover rates remain high as people move seeking better opportunities, property crime rates will remain high. Policymakers and criminal justice professionals may be advised and reminded of the effect of these economic and demographic changes, as well as the effect of reporting changes, on official rates property crime.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".