Bibliographic record
Abstract
Abstract Are the predictable associations between sound and darkness, night and music, based solely on the ability of the aural sense to focus thanks to a reduction of the visible field? Even if the answer lies in a correlation between physical manifestation and physiological adaptation, the socio-cultural scaffolding that stems from this simple fact is of interest. Sites of investigation: John Oswald’s pitch black performances; Studio 303’s Noises from the Dark series; Adrian Piper’s Untitled Performance at Max’s Kansas City; Andre Lepecki’s (and by extension Fred Moten’s) ‘shared aurality’ active in the quartet of darkness/blackness/potentiality/freedom; Derek Jarman’s Blue and, especially, Akira Mizuta Lippit’s analysis of the film where sound becomes image and image becomes sound; the use of darkness at the famed 1938 International Surrealist Exhibition in Paris; the Saydnaya Military Prison; Guy Debord’s Hurlements en faveur de Sade; amongst others. Is sound necessarily of the dark, from the dark, in the dark? Eclipses and shadows, caves and caverns are moments and sites where and when sounds thrive, or at least are invoked and conjured. How does the night sound? Merleau-Ponty begins to answer the question by depicting the night as generator of a different kind of space, one that ‘has no outlines; … is pure depth without foreground or background, without surfaces and without any distance separating it from me’. The implications on sound of the inside/outside blur, the porous muddle, are that its sensorial properties have ontological consequences.
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 | 0.001 | 0.003 |
| 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 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".