A Qualitative Analysis of Parole Suspensions among Women on Parole
Bibliographic record
Abstract
In studying parole outcomes, researchers have pointed to the prominence of technical violations (e.g., breaches of release conditions) in contributing to high rates of custodial returns. In the current study, we contribute to a qualitative understanding of women’s returns to custody in the Canadian federal context. Drawing on casework documents, we analyse pathways to reincarceration by examining circumstances leading up to and surrounding parole suspensions. We conceptualize parole “failure” as a process ( Steen et al. 2013 ) that includes actions, reactions, and decisions on the part of parolees, parole officers, and case managers. Consistent with prior research, we found that parole suspensions were typically tied to non-compliance with parole conditions rather than new offending. However, events prompting suspension were seldom isolated or the first instance of violation. Preceding incidents were typically met with other types of case management response (i.e., enhanced monitoring, verbal intervention, therapeutic responses, and weighing risk against progress). Despite the important role of discretion in responses to violations, we found case management interventions to be largely oriented around managing compliance with parole conditions. We discuss the implications of condition-centric parole governance in relation to underlying principles of responsive case management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".