A Sufficiently Advanced Racket: Performance on the Margins of Art and Commerce
Bibliographic record
Abstract
Abstract: In 2014, the indie house band Vulfpeck released the album Sleepify on the digital music-streaming service Spotify. The album consists of ten tracks, the first titled “z,” the second “zz,” the third “zzz,” and so on. Each track is just over thirty seconds long, and each is composed of absolute digital silence. Not to be confused with John Cage’s only allegedly “silent” composition 4′33″ (1952), as Vulfpeck’s Jack Stratton is quick to clarify, Sleepify is actually, definitively silent: “It’s not a recording of a still environment; it’s an actual digital file full of zeroes.” The catch: via each listen the album accrued on Spotify, the band was that much closer to funding its 2014 tour—which, should all go according to plan, Vulfpeck’s fans would be able to attend for free. Putting Sleepify in conversation with producer Mike Batt’s alleged plagiarism of 4′33″ and performance artist Marina Abramović’s 512 Hours of doing nothing, I interrogate the aesthetic and economic dynamics of the performer-audience relationship in work in which it is only through the audience’s willingness to do something that the artist’s goal toward doing nothing might be realized.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".