Silencing Prisoner Protests: Criminology, Black Women and State-sanctioned Violence
Bibliographic record
Abstract
Protests and resistance from those locked away in jails, prisons and detention centers occur but receive limited, if any, mainstream attention. In the United States and Canada, 61 instances of prisoner unrest occurred in 2018 alone. In August of the same year, incarcerated men and women in the United States planned nineteen days of peaceful protest to improve prison conditions. Complex links of institutionalized power, white supremacy and Black resistance is receiving renewed attention; however, state-condoned violence against women in correctional institutions (e.g., physical, sexual and emotional abuse, and medical neglect by prison staff) is understudied. This qualitative case study examines 10 top-tier Criminology journals from 2008-2018 for the presence of prisoner unrest/protest. Findings reveal a paucity of attention devoted to prisoner unrest or state-sanctioned violence. This paper argues that the invisibility of prisoner unrest conceals the breadth and depth of state-inflicted violence against prisoners, especially marginalized peoples. This paper concludes with a discussion of the historical legacy and contemporary invisibility of Black women’s resistance against state-inflicted violence. This paper argues that in order to make sense of and tackle state-condoned violence we must turn to incarcerated individuals, activists, and Black and Indigenous thinkers and grassroots actors.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.021 | 0.028 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".