Spatially Varying Unemployment and Crime Effects in the Long Run and Short Run
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.
Bibliographic record
Abstract
The Cantor and Land model of unemployment and crime separates the effects of long- and short-run unemployment. In the long run, increases in unemployment are expected to increase crime, whereas the same increases are expected to decrease crime in the short run. This model has been tested for decades, generally supporting these predictions. In this article, we investigate spatial variations in these relationships using geographically weighted regression. Using crime data from Vancouver, Canada (commercial burglary, residential burglary, mischief, theft, theft from vehicle, theft of vehicle, and aggregate property), we find global models do not exhibit statistically significant unemployment–crime relationships, but they do emerge in local (geographically weighted) regression. These results have important implications for theoretical development, policy formation, and policy evaluation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it