Trans embodiment in carceral space: hypermasculinity and the US prison industrial complex
Bibliographic record
Abstract
Queer geographers have recently begun to examine the lives of transgender persons, a heretofore gap in the literature. This article examines the experiences of incarcerated trans persons in the USA, thus extending this nascent trans geography work by considering a new population in a new space. As some scholarly and activist research has shown over the last decade or so, US trans persons are incarcerated at a disproportionately high rate and face harsh conditions while imprisoned. First-hand accounts of trans prisoners' experiences are, however, limited due to the difficulty of accessing this population for research purposes. Working in cooperation with a Montreal-based organization that facilitates pen-pal communications between queer persons inside and outside penitentiaries in the USA, we conducted qualitative research with 23 trans feminine individuals confined in facilities in several states. Our findings unfortunately corroborate the findings laid out in the small existing literature on trans prisoner issues, demonstrating that they endure harsh conditions of confinement. We detail these conditions here, while also pointing to informant responses that offer insight into the ways in which trans incarcerated persons cope with the hypermasculine and heteronormative environment of the US prison. These results are offered in the spirit of advancing a queer abolitionist politics that centers the knowledge and experiences of trans incarcerated persons.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".