Bibliographic record
Abstract
This essay explores Joyce’s attempt, in “Sirens”, to give articulation to the sounds made by objects and nonhuman beings, with the ultimate goal of destabilizing the boundary separating the human voice (and other forms of human expression) from nonhuman sound. The episode itself can be read as a catalogue of sounds, nonhuman and human, that interact with one another in the absence of a qualitative standard of judgment that would separate the human voice from nonhuman sound, music from “noise”, or conceptual language from sonic expression. Human characters in the episode become what Vike Martina Plock has called “soundboards”, or resonating bodies through which the sounds of their material environment achieve expression. Additionally, human bodies are fragmented metonymically into their sounding body “parts” detached from the unity of the human subject, which allows for new forms of sonorous collaboration between sounding objects and sounding body parts. Nonhuman sounds persist in contrapuntal relation with the voices and sounds of the human characters (and their sounding body parts), a phenomenon which forces us to expand our conception of the fugal form of the episode to include nonhuman entities as collaborators, or “voices”, within it. In this way, “Sirens” asks us to consider sound, and by extension music, not simply as the purely intentional product of a human consciousness, but also as a collective composition between human bodies (and body parts) and the sonic materials of their environment.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.025 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".