Risk of what? Risk to whom? The realities of parole practices
Bibliographic record
Abstract
Parole is vital for reintegrating prisoners into society, yet many are excluded due to risk-averse practices. Our research explores how correctional officers, parole candidates, parole board members, and halfway house workers perceive risk, and how these perceptions influence parole decisions. We combine and analyse data from three sources: (1) observations and (2) interviews with 30 correctional officers, 18 halfway house workers, 33 inmates, and 11 board members, alongside (3) case file data from 3,161 prisoners in Quebec provincial prisons. Our analysis shows correctional officers view risk based on the needs of the person who is incarcerated, halfway house workers focus on staff safety and programme stability, incarcerated individuals see risk as their likelihood of breaking conditions, and board members consider societal risk. Quantitative data underscores the role of risk in decisions: 90% of parole-recommended individuals are low-risk, 65% who waive parole are high-risk, and 93% of low-risk individuals are granted parole. These different visions of risk and the incompatibility between high risk and release have brought risk assessments to deviate from their initial purpose, which was to assess needs and adjust interventions accordingly. They are instead used to deny parole to those who would most benefit from gradual release.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".