Too Many Sounds, Too Many Sensations: Shifting Bodies Towards a Viral Being
Bibliographic record
Abstract
Through this article we present an experimental method that focuses on attuning our bodies to other bodies, human and more-than-human, through sound. We suggest sounded thinking as a way to think beyond the dualisms of a thinking versus feeling body, and as a form of transindividual solidarity in times of uncertainty. Methodologically, we draw from Robin Nelson’s idea of going against the primacy of data and evidence-based methods to propose a collaborative practice of Sound Journaling and a Sounded Improv that implicates our sounding human bodies with other sounding bodies, human, beyond human and material, as instruments of research in an attempt to situate this practice-based research inquiry as a resonance between theory and practice as praxis (Nelson, 2015). In our analysis, sound, bodies, and movement become focal points through which we attempt to parse what sounded thinking is and how it can be taken up in the everyday.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".