<title>Verses Witness<subtitle>Reimagining Carceral Spaces through Poetry
Bibliographic record
Abstract
Despite growing global concern over mass incarceration and carceral capitalism, the architecture of imprisonment remains largely inaccessible, obscured and inscrutable, even to prison reformers and designers. In this context, first-hand accounts by prisoners are crucial for revealing the material and affective dimensions of carceral space, especially when conveyed through literature. Over the centuries, many imprisoned figures have turned to prose and poetry to express their struggles and spatial experiences, inscribing confinement onto their bodies and words. From Oscar Wilde to Nazim Hikmet, Mahmoud Darwish to countless unnamed voices, prison poetry has served as both testimony and resistance, offering a powerful lens into the lived realities of incarceration and forming a transhistorical, transgeographic archive of carceral experience. This article focuses on Canadian poet Bradley Peter’s Sonnets from a Cell (2023) in particular to examine how prison poetry can illuminate the psychological, material, and architectural contours of confinement. Through a literary and spatial reading of Peters’ work, it explores how poetic expression maps the lived experience of confinement and reveals how bodies perceive, endure, and resist carceral design. Grounded in abolitionist thinking, the article proposes a dialogue between poetry and architecture to reimagine carceral spaces and ultimately question their continued existence and the role of prison designers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.045 | 0.007 |
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".