A meta-analysis of the impact of community policing on crime reduction
Bibliographic record
Abstract
Over the last few decades, many studies have been conducted to understand whether community policing (CP) has an impact on reducing crime rates. Yet there is still substantial controversy surrounding the question of the impact of CP on crime rates. Despite the broad understanding of CP, various types of measurement of crime statistics have led research- ers to conduct meta-analyses of the phenomenon. This study combines two previous meta-analyses of CP and Turkish and English online searches. We used the Comprehensive Meta-Analysis (CMA 3.0) statistical program to calculate the effect sizes of previous studies. We employed odds ratio (OR) as the effect size, since it is one of the most appropriate methods for proportions. We found no evidence suggesting that CP has an impact on reducing disorders, drug sales, or property crime, but it does have an impact on reducing crimes such as burglary, gun use, drug use, Part I crimes, and robbery, as well as fear of crime. Depending on crime type, CP can be a promising policing strategy to reduce crimes. und a statistically significant, positive impact of CP, despite the limitations of including only Turkish- and English-language studies.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".