Statistician’s Blues: A Methodological Critique of Measuring the Association between Police and Crime
Bibliographic record
Abstract
Melanie S.S. Seabrook and colleagues (Police funding and crime rates in 20 of Canada’s largest municipalities: A longitudinal study, Canadian Public Policy 49(4) (2023): 383–98) have investigated the relationship between per capita police budgets and crime for 20 of Canada’s largest municipalities. The authors state that there is no consistent relationship between these two variables and question the utility of increased police spending. Their questioning of increased police spending is not necessarily unjustified, particularly given the expansive research that demonstrates that investing in prevention is far more cost effective. However, we scrutinize their data and methods to make such a claim. After reproducing their results, we use more appropriate data and methods to interrogate this relationship. We find support for an association between police officers per capita and lower crime but caution against any causal connection. We discuss the implications of this relationship, if present, and alternative ways to address crime and its prevention.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".