The Resonate Prison: Earwitnessing the Panacoustic Affect
Bibliographic record
Abstract
Panacoustic surveillance can be low-intensity and mundane, but when taken to its extreme, it is coordinated with physical violence to create an atmosphere of hallucinatory fear. Our entry point into this problem is through a case study of the Saydnaya torture prison in Syria, a terrifying and opaque architecture of power. This short paper draws from the earwitness art and human rights activism of Lawrence Abu Hamdan concerning Saydnaya in collaboration with Amnesty International: from our analysis of the prison, we extrapolate lessons of panacoustic technologies more broadly, which are not necessarily or immediately violent but nonetheless disempower subjects by constraining their behaviors and rendering walls indefensibly porous. In developing a nascent theory of panacoustic surveillance, this paper makes two distinct contributions to surveillance studies. First, it puts sound and surveillance studies scholars into dialogue to echo Hamdam’s argument that walls do not represent an absolute barrier but a corporeal medium by which power and knowledge can permeate and reflect as vibration. Second, our discussion articulates a politics of transparency and accountability that helps rethink notions of actuarial surveillance as not only a form of top-down statistical and biopolitical monitoring and governance but also as a means of developing panacoustic audits that seek to hold governments and other human rights abusers to account.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.023 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".