Strategic masculinities: Vulnerabilities, risk and the production of prison masculinities
Bibliographic record
Abstract
Expressions of masculinity in prison are most often characterized as being structured in response to an environment that encourages displays of stoicism, bravery, physical prowess and violence/aggression. However, we found that the antagonistic, precarious and risk-prone environment of the prison shapes prisoners’ behaviours and the constitution of ‘normative’ and hegemonic masculinities in more nuanced ways than prior research suggests. Drawing on in-depth interviews with 56 male parolees, we explored how these men perceived and responded to risk while incarcerated, as well as how prison masculinities are linked with experiences and management of risk to their personal (legal, physical and emotional) safety. In this article, we focus on how prisoners mobilized and negotiated their masculine subjectivities to handle the uncertainty of imprisonment and the various risks they encountered in prison. We argue that penal risks and prison masculinities are mutually constitutive; risk is linked to perceptions of physical and emotional vulnerability, which shape prisoners’ masculine embodiment. Simultaneously, prisoners try to respond to uncertainty and perceived risk in ways that present their masculinity as empowered rather than submissive. Our findings advance the conceptualization of prison and hegemonic masculinities, penal environments and risk/uncertainty.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".