“No Hostages through These Doors”: Thomas Bartlett Whitaker’s “Hell’s Kitchen” and the Politics of PEN
Bibliographic record
Abstract
Abstract: PEN International, one of the world’s first human rights organizations, has long defended the free speech of persecuted writers. The PEN Prison Writing Program, however, has a slightly different agenda, which is to help convicted criminals become writers. The PEN Prison Writing Program “believes in the restorative and rehabilitative power of writing” and encourages “the use of the written word as a legitimate form of power.” But what is the nature of the “power” of the written word? And what, moreover, will this power restore and rehabilitate? When PEN proclaims the “power” of the written word, are they honouring an important strand of America’s liberal intellectual heritage? Are they pledging allegiance to a romantic coupling of art and freedom? Are they inadvertently helping to bind prisoners ever more insidiously to the carceral regime? Or are they claiming something that is actually true? This article addresses these questions through a reading of Thomas Bartlett Whitaker’s prize-winning essay “ Hell’s Kitchen ” and finds that the power of Whitaker’s prison writing resides in its capacity to shock readers into a sensory appreciation of the radical strangeness of life lived in a state of civil death.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.039 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".