Bibliographic record
Abstract
Steve McCaffery describes sound poetry as a “new way to blow out candles” and “what sound poets do.” In his brief survey of sound poetry, McCaffery describes the genealogy of sound poetry from its earliest formalized birth during Russian futurism (found in the experiments of Khlebnikov and Kruchenykh) and builds his survey until North America, 1978. This essay will consider the history of sound poetry, a history that has no history, but retains the avant-garde experimentalism of modernist poetics. By looking at sound poems by Raoul Hausmann and Kurt Schwitters; the sound-experiments of Diamanda Galás; performance in sound poetry; the influence of “primal therapy” (which emphasizes the therapeutic potential of the scream); and the theological tradition of glossolalia, I will demonstrate how the noisiness and non-sense of sound poetry offers a variety of forms of political engagement against hegemonic uses of sound and silence. Sound poetry is notable in that it is loud – originally being called Lautgedichte or literally “loud poems” – and this brash noise opens up a heterotopic space of acoustic potential: of potential sonic engagement outside of normative chirps, whistles, vocalizations, glottal stops, fricatives, and speech. This “sonic engagement” will be grounded in the new theoretical concept of what I call "arche-speech" or "arche-sound."
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".