Bibliographic record
Abstract
IntroductionWhether we notice or not, our days are fi lled with listening.Of course, you will object: some people more than others, some countries more than others, some economies more than others, and this is true.But a colleague told me he heard music in a supermarket on a dirt road in South Africa, so let us not leap to conclusions about the lives of others. 1 Nonetheless, I will be happy here to think about England and the United States, the two countries where I've lived, and to a lesser extent, Canada and western Europe, where I have frequently traveled and have discussed these issues with colleagues and students.Ubiquitous Listening is about the listening that fi lls our days, rather than any of the listenings we routinely presume in musicology, sociology, media studies, and elsewhere.The problem I am addressing is not a disciplinary one-it crosses fi elds and disciplines blithely.How do we listen to the music we hear everywhere, and how does that listening engage us and activate the world we move in?My basic thesis is this, put bluntly: Ubiquitous musics, these musics that fi ll our days, are listened to without the kind of primary attention assumed by most scholarship to date.That listening, and more generally input of the senses, however, still produces affective responses, bodily events that ultimately lead in part to what we call emotion.And it is through this listening and these responses that a nonindividual, not simply human, distributed subjectivity takes place across a network of music media.Since these six terms-ubiquitous musics, affect, the senses, attention, listening, and distributed subjectivity-are at the core of everything that follows, they bear some defi ning.xii / Aristides is a typical 13-year-old boy.He plays basketball after school, is learning the clarinet, and in the evening sits in front of his computer playing games.There is one game that he is especially keen
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.565 | 0.385 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".