THE GRANDE PRAIRIE PACT PROGRAM EVALUATION: DISCREPANCY BETWEEN MODEL EVALUATION PRACTICE AND CONSTRAINED REAL WORLD EVALUATION OF CRIME PREVENTION IN SMALL COMMUNITIES
Bibliographic record
Abstract
This article discusses and demonstrates the discrepancies between ideal, theoretical program evaluation processes and real world evaluation practice, which is constrained by numerous and varying factors. The article describes the real world experience of Mount Royal University’s Centre for Criminology and Justice Research researchers in conducting an evaluation of the Police and Crisis Team (PACT) in Grande Prairie, Alberta, including a Social Return on Investment (SROI) analysis. PACT, which partners an Royal Canadian Mounted Police (RCMP) officer with a mental health professional, represents a blend of secondary and tertiary crime prevention and attempts to diminish crime in the community by addressing the risk factors of individuals with mental health concerns (creating trust with individuals, increasing awareness of resources, and decreasing stigmatization in the community). PACT also specifically targets those individuals with mental health issues who are in contact with the law to try to decrease recidivism and increase community safety. Challenges were present in the evaluation due to the time frame, staff turnover, program start-up issues, and confidentiality and sensitivity of the program focus. Despite the challenges, the CCJR team completed an evaluation including a forecast SROI, identifying several successes, challenges, and recommendations for change.
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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 | 0.230 | 0.283 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".