Parole Work in Canada: Tensions in Supervising People Convicted of Sex Crimes
Bibliographic record
Abstract
Internationally, parole work is loaded with tensions, particularly when supervising a people convicted of sex crimes (PCSCs) who, due to their criminal history, are stigmatized and occupy the lowest rungs of the status hierarchy in prison and society more broadly. Drawing on analyses of interview data from federal parole officers ( n = 150) employed by Correctional Service Canada, we interpret their perceptions and feelings about overseeing re-entry preparations and processes for the PCSCs on their caseloads. We unpack the “tensions” imbued in parole officers’ internal reflections and negotiation of complexities in their efforts toward supporting client’s rehabilitation efforts, desistance from crime while negotiating external factors (e.g., the lack of available programming), and being responsible for supervising PCSCs. We highlight facets of occupational stress parole officers experience, finding PCSCs may be more compliant when under supervision but may also require more of a parole officer’s resources, including time and energy. We put forth recommendations for greater empirical nuance concerning parole officer work and their occupational experiences and beliefs about PCSC, particularly as related to parole officer health.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".