The Priority Prolific Offender Program: Preliminary Findings from the First Year of the Process and Outcomes Evaluation
Bibliographic record
Abstract
The Priority Prolific Offender Program (PPOP) joins a growing number of programs in Canada designed to collaboratively address the behaviour of prolific offenders, using a combination of monitoring, enforcement, and rehabilitative services. The objectives of Alberta’s program specifically aim to ensure Crown prosecutors have complete, accurate, and up-to-date information on prolific offenders, rehabilitation is promoted through the provision of appropriate support services, and the consequences of offending and reoffending are meaningful. The Canadian Research Institute for Law and the Family (CRILF) was first contracted in 2010 to conduct a process evaluation of the program. The results of this evaluation yielded five recommendations for the program moving forward: (1) The development of an Operations Manual; (2) A review of the comprehensive bail packages; (3) Adequate funding and resources; (4) Education and awareness; and (5) A process and outcomes evaluation. In 2012, PPOP followed through with the Phase 1 process evaluation’s recommendation to continue the process evaluation and conduct a thorough outcomes evaluation. The comprehensive three-year research strategy proposed by CRILF has three major components: (1) a process analysis designed to monitor and document program implementation, and specifically, to assess whether the Phase 1 process evaluation recommendations have been addressed; (2) an outcome analysis designed to track anticipated changes that result from the activities of the program; and (3) a cost and potential benefit analysis of the program. The purpose of the current report is to discuss the findings of the first year evaluation activities. The objectives of the report were: (1) To re-visit PPOP processes, and assess the current processes in relation to the recommendations provided in the Phase 1 process evaluation; (2) To report preliminary findings from the retrospective data collection; and (3) To make process recommendations for the program as the evaluation enters its second year. Three main methods were employed for this report: a retrospective analysis of data on clients de-selected from the program from its inception to December 31, 2012; a survey of PPOP staff; and a survey of PPOP stakeholders.
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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 | 0.064 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".