Bibliographic record
Abstract
Sound has always been a material issue in prisons, whether it be in connection with sonic surveillance, the “silent cell,” or the insistence of sound (excessive noise, counter-carceral music making). This article asks: How and when does the carceral soundscape become a litigable issue? Our article opens with a discussion of the challenges involved in attempting to study the sonic ambiance of the penitentiary through the medium of written documents and proposes a methodology of “sensing between the lines” by way of a solution. It goes on to analyze the “moral architecture” at the foundation of the modern prison in an effort to excavate the sonic dimensions of incarceration in the context of a system that was designed with silence at its core. Solitude and silence were presumed to have an “emancipatory effect” on the prisoner by attuning the carceral subject to “the inner voice of conscience” through forced withdrawal from the distractions of the senses. The next part considers the ways that, despite attempts to manage sound, its insistence has resisted these forms of control. It presents solitary confinement as a crucial site to explore the ways in which enforced silence, as an organizing principle, has undergone several contortions that gave rise to alternative rationales such as “structured intervention,” yet has persisted. The article then explores how this enduring silence has figured in the contemporary case law, alongside other forms of acoustic violence, such as excessive noise and sonic resistance to the conditions of incarceration on the part of prison inmates (e.g., rapping to beat the rap). While some cases describe the experience of the prison as one of unbearable silence, others describe it as noise without respite. This research highlights the ways that sound in prison has remained an important site of discipline and contestation that reverberates through the case law, yet without being appreciated adequately by the courts. The article concludes with observations about the ways that probing the role of sound in the logic of incarceration can complement litigation efforts that question carceral logics.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.016 | 0.048 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".