WHAT DOES THE INTERNET SOUND LIKE?
Bibliographic record
Abstract
When it comes to the study of internet phenomena, the visual constitutes a privileged mode of analysis, as seen by the ample and storied scholarship on visual media dn visually based methods. In order to disrupt this “hegemony of the visual,” and explore alternate ways of knowing and being online, this panel bring internet studies scholarship into meaningful dialogue with sound studies scholarship and poses the provocation: “how can sound be used methodologically in order to expand and deepen our understanding of the internet?” This topic is addressed in a variety of ways by the contributing authors. The first paper of this panel introduces the concept of deep listening as a feminist analytical method for “tuning-in” to the persistent hegemonic structures that underly digitally mapping technology. The second paper presents the concept of “the sonic interface” as a means to examine how sound mediates our everyday interactions with the internet. The third paper proposes the use of participatory arts based methods as a reflexive and critical framework through which researchers can begin to “listen” to alternative internet narratives while centering the voices of communities rather than researchers. The final paper deploys sound as a method for studying social behaviour on TikTok, and demonstrates the widespread applicability of sonic methods in the wider study of internet phenomena. All in all, this panel aims to highlight the immensely generative potential of centring sound in our interrogations of the online and the digital.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".