Bibliographic record
Abstract
New empirical insights into Canadian policing are derived from publicly available panel data collected by Statistics Canada between 1998 and 2017 across almost 700 Canadian municipal police jurisdictions. Canadian police jurisdictions that hire more officers tend to experience less crime overall, including less property crime specifically. Each additional Canadian police officer correlates with slightly fewer homicides and 13.3 fewer reported property crimes on average, including 2.9 fewer burglaries and 3.7 fewer stolen vehicles annually. The results cannot be explained away by time-invariant jurisdiction-specific factors, population growth, or other time trends common to all jurisdictions. In elasticity terms, a 1% increase in Canadian police staffing is associated empirically with reductions of 0.93% in homicides, 0.44% in property crimes, 0.63% in burglaries, and 1.37% in vehicle thefts. Purely in terms of crime reduction and reduced victimization across these crime types, it is estimated that the typical Canadian police officer has the potential to generate a marginal benefit to society worth more than $114,000 annually. Taking into account unreported property crime would increase the marginal benefit to society up to $198,000. This new evidence confirms that public investments into local policing can contribute to the reduction of crime and can yield social benefits that exceed their costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 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) | 0.015 | 0.001 |
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".