Bibliographic record
Abstract
The Waterloo Regional Police Service’s Community Resource Officer (CRO) program is a 911 call diversion program that redirects high-need individuals from traditional policing towards a specialized police unit. This unit was designed to decrease incidents reported by services and community members by connecting program users to existing community services. Through conducting an impartial evaluation, this study hopes to determine the CRO program’s effectiveness and improve the CRO program and its utilization of associated programs. This study will perform and analyze interviews of stakeholder opinions and feedback and create a program logic model for future program development and evaluation.Interviews were conducted with 12 CROs and five social service employees. To determine the effectiveness of the program, a second phase of the study will be required which will include program user opinions and quality indicator development. Based on phase one interviews, a logic model was created, and strengths and weaknesses were analyzed. Program strengths include connections to services, access to the target population and adaptability. Some program weaknesses include low community awareness, low resources for community needs, and vague roles/responsibilities. These weaknesses can be resolved through external publishing, increasing resources, formalizing the program, and additional training.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".