(Dis)Guised Punishment: Examining the Consequences of Post-Release Management Programs
Bibliographic record
Abstract
Abstract This paper examines the consequences of post-release management programs, arguing that these initiatives extend penal power beyond formal sentencing through mechanisms such as surveillance, discretionary policing, and information sharing. While prolific offender programs are framed as risk-management strategies rather than punishment, they operate in ways that mirror carceral control, restricting autonomy and increasing individuals’ susceptibility to criminalization. Drawing on qualitative interviews with crime analysts and police officers, I analyze how the prolific label structures police interactions, justifies heightened scrutiny and reinforces recidivist assumptions that shape sentencing and enforcement decisions. The findings challenge clear-cut distinctions between carceral and non-carceral interventions, highlighting how penal control functions fluidly across legal and administrative domains. By linking empirical findings to broader theoretical discussions of punishment, surveillance, and risk governance, this study contributes to ongoing debates on the expansion of state power in contemporary criminal justice.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".