The Struggle over How to Help Incarcerated People: A Field Analysis of the Penal Voluntary Sector
Bibliographic record
Abstract
ABSTRACT This paper examines the penal voluntary sector (PVS) in Canada, where (un)paid staff act as “first responders” and “last resorts” for the complex, often intractable problems associated with incarceration. Like many other social problems, there are no single or straightforward solutions for the harms related to imprisonment. As such, the PVS’s work is marked by tensions, uncertainties, and competing visions about what should be done. Moreover, scholarship has provided little guidance on how to make sense of diverse and often conflicting ideas about helping incarcerated people. To conceptualize these efforts collectively, this paper draws on online archival research to characterize the PVS as an interstitial field sitting among the voluntary sector, the academic field, and the penal field. Envisioning the PVS in this way offers a new vantage point from which to understand how its actors position themselves as authorities in the struggle over how to help incarcerated people, as well as to understand the ideas, symbols, and resources they rely on to justify their perspectives. By examining these various claims to authority and their interrelationships, this paper reveals the underlying assumptions, logics, and structures of power that set the boundaries for the PVS’s work with incarcerated people.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.030 | 0.039 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".