Credence for clearance rates: modeling police clearance rates in Canadian municipalities
Bibliographic record
Abstract
Clearance rates are one of the most common ways by which police performance is assessed. Next to crime rates, clearance rates are among the few metrics used to assess investigative success across jurisdictions. Low clearance rates are also often used, by police leaders and police services boards alike, to petition for additional staffing. However, the correlation between staffing levels and clearance rates has not been well studied in the Canadian context. In this study, we examine clearance rates across 82 jurisdictions in Canada over time (2000 – 2023) to determine if police staffing levels impact clearance rates using fixed effect panel regression, controlling for several relevant factors. We find a significant, positive relationship between police staffing and clearance rates. However, this relationship is weak, and our findings suggest that the substantial cost of additional police may not be worth the minor improvement to clearance rates.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".