Correctional Officer Reflections on How to Address Incarcerated People’s Unmet Needs in Canadian Prisons
Bibliographic record
Abstract
Although incarcerated people best understand their needs and rehabilitative processes, the current study explores how correctional officers (COs) understand the needs of people in their custody and care to support their reentry and incarceration experience. Given COs are the lifeline of incarcerated people, penal scholarship should value their input as an essential resource for recognizing what incarcerated people need to desist and prepare for reintegration when imprisoned. Drawing on qualitative interviews with 28 COs employed in Atlantic Canada, we contextualize and report on what COs believe are the (often unmet) needs of incarcerated people. More specifically, intending to learn what COs desired in the design of a new replacement prison in a Canadian Atlantic province, we found COs emphasized meeting prison residents’ unmet needs – often over their own – with results centralizing incarcerated people’s physical and mental health needs, availability of meaningful rehabilitative, vocational, and cultural-specific programming, and access to recreation and fresh air. We discuss the actualization of each programmatic construct, recognizing prison society is unique and programming and practices must respond to the positionality of incarcerated people and their lived experiences rather than normative ideals about social living.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.042 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".