WHEN, WHERE, AND HOW IS DIGITAL SOUND?
Bibliographic record
Abstract
This panel’s first author, in discussing podcast archiving, notes that internet archives like the Wayback Machine have had much more focus on preserving visual and text content than sound. Internet Research has similarly traditionally had less engagement with sound than with other forms of digital content. This panel seeks to contribute to ongoing work to bring Sound Studies and Internet Studies into better conversation with each other, taking digital sound as a common object and examining it in different cases and through different methods to provide a richer understanding of the role sound plays in shaping our online experiences. The papers coalesce around their common object of inquiry, digital sound, providing depth of understanding about the subject matter by approaching from different directions. Moreover, the papers help to illuminate each other by taking different approaches to common themes. The first and second papers raise key questions about who tends to be included and excluded in circuits of production as well as whose digital sound tends to be seen as valuable. Papers 1, 2, and 3 all ask about how, despite rhetorics of democratization and variety, forms of digital sound may be becoming standardized through technological and social means. The first and third papers call attention to the ways the specific affordances of given digital production technologies shape (though do not determine) the kinds of production that become prevalent in a given moment. There are also methodological convergences: papers 3 and 4 take as their object of inquiry technology makers, and papers 2 and 4 both use press coverage as the site of investigation. Finally, papers 2 and 4 ask questions about what people believe is socially proper or correct in the case of digital sound. In these ways, this panel represents both an important contribution to our understanding of contemporary issues in digital sound as well as relating to broader questions central to internet research.
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How this classification was reachedexpand
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".