Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
How do we thoroughly historicize the voice, or integrate it into our historical research, and how do we account for the mundane daily practices of voice . . . the constant talking, humming, murmuring, whispering, and mumbling that went on off stage, in living rooms, debating clubs, business meetings, and on the streets? Work across the humanities has provided us with approaches to deal with aspects of voices, vocality, and their sounds. This article considers how we can mobilize and adapt such interdisciplinary methods for the study of history. It charts out a practical approach to attend to the history of voices—including unmusical ones—before recording, drawing on insights from the fields of sound studies, musicology, and performativity. It suggests ways to “listen anew” to familiar sources as well as less conventional source material. And it insists on a combination of analytical approaches focusing on vocabulary, bodily practice, and the questionable particularity of 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it