The prisoner’s dilemma: How male prisoners experience and respond to penal threat while incarcerated
Bibliographic record
Abstract
Drawing on interview data with 56 former prisoners in Canada, we examine how male prisoners understand, experience, and respond to threat while incarcerated. We show that prisoners face a variety of different and often competing threats, resulting from prisoner interactions (e.g. threat of physical violence for being a “snitch”) on the one side, and institutional powers and procedures on the other side (e.g. threat of delayed release from prison). These threats are competing insofar as countering a prisoner threat opens the door to threat on the institutional level (i.e. administrative uncertainties) and vice versa. As a consequence, we show how feeling threatened for prisoners becomes paramount and in many cases unavoidable as the different threats in prison are difficult, if not impossible, to handle in unison. However, in an effort to stay physically safe and work toward their release, prisoners must find viable strategies to navigate different prison environments, particularly as they move between prisons of differing security classifications. We draw on Giddens' notion of “ontological insecurity” to draw attention to prisoners' feelings of perpetual vulnerability and insecurity. In addition, we build on Luhmann's conceptualization of risk and danger to explain how male prisoners experience and respond to moments of “danger” when they are faced with competing threats and must decide how to best navigate them.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".