Bibliographic record
Abstract
There is something abo ut sound th at is somehow beyond our control. This is partly to do with how we sense sound as compared with how we sense the world in other ways. We can close our eyes and not see the world, but our ears are not equipped with the same ability to control reception. This is also to do with the relationship of our hearing to our other senses. Even if we plug our ears, sound still reaches us through vibration, or if we somehow manage to ignore vibration, there is still this sense of listening to some thing within, unintended - perhaps the sound of being. Sound is entirely an imrn crsivc experience; it surrounds us and demands that we be present to its sensation; or, as sound researcher Douglas Kahn has said, “Terrestrially, sound is not only experienced as occurring in between but as surrounding the listener, and the source of the sound is itself surrounded by its own sound. This mutual envelopment of aurality predisposes an exchange among presences” (27). All communication can be more or less be summed up this way, as an “exchange among presences,” and yet, when we consider the multiple, layered and unpredictable ways in which this exchange is manifested through sound, we must consider the noise of such relationships; we must consider what noise can tell us about being in the world.
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.152 | 0.082 |
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".