‘Mobilizing’ prisoner reentry research: Halfway houses and the spatial-temporal dynamics of prison release
Bibliographic record
Abstract
To date, prisoner reentry has been treated as a generic process—that is, people ‘reenter,’ without much specification regarding when and/or where this ‘reentry’ occurs. Drawing on in-depth interviews with halfway house residents in a north-western Canadian city, this article seeks to unpack the concept of prisoner reentry by exploring its spatial-temporal dimensions. I conceptualize prisoner reentry as a temporally fragmented sometimes piecemeal process that occurs across time as well as different locales, including neighbourhoods, cities, and sometimes even provinces. I do this by analyzing reentry through the lens of mobilities. Specifically, I argue that reentry via halfway houses produces mobilities that are experienced by former prisoners as simultaneously disciplining and productive of their future. In this context, I highlight the importance of paying attention to ex-prisoners’ conceptions of their future, including when and where they imagine that they will enact different aspects of their reintegration.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| 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".