“Why Doing Good is So Hard”: Examining Community Efforts to Help Criminalized Individuals in Canada
Bibliographic record
Abstract
In the Canadian context, the efforts of charitable and non-profit organizations have long been considered key to understanding both the historical trajectory and contemporary shape of punishment. Recently, scholars have suggested that economic and political developments such as neoliberalism and welfare state retrenchment have meant that these organizations, as well as their broader communities, have taken on larger roles in criminal justice processes. In response, there has been a flurry of research examining the penal voluntary sector. Some scholars have suggested that this sector is part of the archipelago of social control, responsible for widening carceral nets and diversifying penal power, while others have envisioned this sector as agitating for social change, improving public safety, facilitating personal growth, and even saving lives. Few studies, however, have explored questions about the size, scope, structure, and boundaries of these diverse organizations and adjacent community efforts collectively. As a result, although valuable analyses have examined parts of the penal voluntary sector and their practices, little guidance is available with regard to conceptualizing the overall texture of this domain. This dissertation is one step toward clarifying the overarching logic that structures practice among the wide range of organizations, associations, and individuals seeking to help criminalized individuals in Canada. In particular, this research draws from the theoretical work of Pierre Bourdieu and the American pragmatist philosophical tradition to advance a view of this domain as structured around the struggle over who and what are legitimately helping criminalized individuals. It explores how diverse efforts to help criminalized individuals fit together—and where, when, and why they sometimes conflict. In doing so, this dissertation furthers understandings of: (i) the diversity of actors working to help criminalized individuals; (ii) the normative assumptions underpinning this work; (iii) the relationships between individuals, organizations, and groups in this domain; and (iv) the impact of various helping efforts on criminalized individuals. Throughout this research, I mobilize four types of qualitative data as evidence: fieldnotes from ethnographic participant observation in penal voluntary sector organizations; semi-structured interviews with volunteers and staff at these organizations; organizational documents obtained through fieldwork; and publicly archived materials.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.038 | 0.010 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".