Assigning Value to Peel's Regional Police’s School Resource Officer Program
Bibliographic record
Abstract
Police in schools In an era where the costs of policing are constantly under scrutiny from governing municipalities, the time has come for police agencies to re-evaluate the services they provide. To do this, they need to answer questions relating to the value that different activities they perform create in the communities they serve. In other words, they need to change the focus of the conversation from “what does this service cost” to “what value does this service provide.” \n \nThis document summarizes key findings from a longitudinal (2014-2017), multi-method (quantitative, qualitative, and ethnographic analysis, along with a Social Return on Investment [SROI] analysis) case study undertaken to identify the value of School Resource Officers (SROs) that are employed by Peel Regional Police and work in the service’s Neighborhood Police Unit (NPU). Of note is the application of SROI techniques in this evaluation process. SROI, a methodology that emerged from the not-for-profit sector, helps researchers identify sources of value outside of those considered through traditional valuation techniques, such as cost-benefit analysis. \n \nEvaluation of Peel Police’s SRO program was motivated by a number of factors. First, the costs of this program are both easy to identify and significant (just over $9 million per year). Second, it is very challenging to identify the value that this program provides to students and the community. The challenges of quantifying the value offered by assigning full-time SROs to Canadian high schools is evidenced by the fact that such programs are rare, as police services around the world have responded to pressures to economize by removing officers from schools and either eliminating the role of the SRO or having one officer attend to many schools.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".