“I’ve Had Cases That Have Gone in the Wrong Direction and That Has Affected Me”: A Qualitative Examination of Decision Making, Liminality, and the Emotional Aspects of Parole Work
Bibliographic record
Abstract
Parole officers are central actors in the penal system as their decisions can affect the timing of a person’s release from prison and also restrict or enable their freedoms in the community upon release. Existing research on parole examines how parole officers think about and govern ex-prisoners via techniques of surveillance, regulation, and support. Few studies, however, provide qualitative insight into how parole officers experience their occupational authorities and associated power over (ex)prisoners’ future, or the emotions generated by frontline supervision work. Using data from interviews with 150 parole officers in Canada, we explore the emotions associated with parole officers’ occupational responsibilities and authorities vis-à-vis the parolee, the public, and the parole officer’s employer. Participants experienced their duty to make decisions that impact their clients’ legal and social futures, and potentially public safety, as a source of emotional stress and concern, as they worried about how their decisions could negatively affect their client, the community, and their own professional status. In illuminating parole officers’ feelings and experiences, we show how parole—the “transition” between incarceration and freedom—produces an emotionally charged experience not just for (ex)prisoners, but also for those engaged in frontline supervision work.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.020 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".