Shaping the road to reentry: Organizational variation and narrative labor in the penal voluntary sector
Bibliographic record
Abstract
Financial austerity, welfare state retrenchment, and the movement towards evidence-based interventions have intensified the pressures on penal voluntary sector (PVS) organizations. The result is an increasingly competitive field of social service provision in which organizations must differentiate themselves in the struggle over funding, contracts, symbolic authority, and potential clients. We explore this struggle by examining the distinct roads to reentry constructed at four PVS organizations in Ontario, Canada. Our analysis initiates a dialogue between individual narratives and organizational discourses, contending that the road to reentry is coauthored among organizations and criminalized individuals—albeit on unequal terms. Our findings reveal that there are significant pressures for criminalized individuals to perform narrative labor to align themselves with organizational understandings of reentry. Such pressures include: the denial of services or social assistance payments, threats of being returned to prison for “inadequate” participation in rehabilitation, and risks of not being considered for coveted “professional ex” positions at PVS organizations. In light of these empirical findings, we also offer a conceptual reflection on the challenges criminalized individuals likely face accessing services from multiple organizations with differing roads to reentry, suggesting that navigating these diverse roads not only requires narrative labor, but also narrative dexterity.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.032 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".