Exploring Barriers to Researching the Economics of Municipal Policing
Bibliographic record
Abstract
Using Ontario municipal expenditure and access to various technologies as an entry point, this article identifies several barriers to and limitations of studying the economics of policing in Canada. We explore several data sources, including the Police Administration Survey, Statistics Canada Census Program, the Uniform Crime Reporting Survey, and municipal and First Nations police service annual reports. We conclude that in an era of evidence-based policing, Canadian researchers and practitioners are unable to explore capital expenditure in any meaningful way because of restrictions on accessing detailed equipment information, as well as limitations of the existing Police Administration Survey. Further, several challenges are associated with identifying land area of jurisdiction and size of population served by municipal police services. Such challenges are heightened in those jurisdictions served by First Nations services.
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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 | 0.027 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".