A Study on the Impact of Actuarial Assessment Tools on Probation Practices in Ontario
Bibliographic record
Abstract
There has been a rising concern surrounding risk within society. This increasing concern has dominated almost all aspects of human life and more specifically the way in which citizens are governed. How risk is addressed in general has shifted significantly; given this, the criminal justice system has also seen an escalation in concerns surrounding risk. Subsequently, there has been a push towards evaluating said risks through the use of actuarial assessment tools. Research has shown that with the rising reliance on actuarial assessment tools came the decrease in practitioner’s ability to rely on their professional judgement when conducting their work. However, there has been a gap identified in the literature. This gap pertains to how practitioners, particularly, probation officers perceive the impact of these actuarial tools on their work. This study aims to analyse how probation officers, within the province of Ontario, view the impact of actuarial assessment tools on their work. This study is guided by the theory of governmentality, as coined by Michel Foucault. In order to explore the impact of actuarial assessment tools on the practice of probation, seven semi-structured interviews were conducted with former probation officers. The perceptions varied and participants did not provide a unique and monolithic response; rather, the voices of all participants were shared to create a larger picture of how actuarial assessment tools impact the work of practitioners in the practice of probation.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.016 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".