“That shit doesn't fly”: Subcultural constraints on prison radicalization
Bibliographic record
Abstract
Abstract Many observers describe prison subcultures as inherently and irredeemably antisocial. Research directly ties prison subcultures to violence, gang membership, and poor reintegration. In extreme cases, research has also suggested that prison subcultures contribute to incarcerated people joining radical groups or embracing violent extremist beliefs. These claims, however, ignore key differences in the larger cultural and social context of prisons. We examine the relationship between prison subcultures and prison radicalization based on semistructured qualitative interviews with 148 incarcerated men and 131 correctional officers from four western Canadian prisons. We outline several imported features of the prison subculture that make incarcerated people resilient to radicalized and extremist messaging. These features include 1) national cultural imaginaries; 2) the racial profile of a prison, including racial sorting or a lack thereof; and 3) how radicalization allowed incarcerated men and correctional officers to act outside the otherwise agreed‐to subcultural rules. Our research findings stress the importance of contemplating broader sociocultural influences when trying to understand the relationship between radicalization and prison dynamics and politics.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".