Pandemic Listening: Critical Annotations on a Podcast made in Social Isolation
Bibliographic record
Abstract
We no longer sound the same to each other, and we listen to the world differently than we did before. This co-written article presents a series of reflections upon the implications of the increasing dominance of audiovisual telecommunication environments for how we have been listening to each other and to the world around us during the COVID-19 pandemic period. The reflections explored in this article were first articulated within a media production: the podcast episode, “How are we listening, now? Signal, Noise, Silence,” as part of The SpokenWeb Podcast produced by Camlot and McLeod during the first months of the pandemic. “Pandemic Listening” revisits questions posed in the podcast episode about the implications of our increasingly pervasive Zoom-based methods of communication, and the connection between how we are listening and how we are feeling, individually and collectively. In revisiting these discussions as they are transcribed and at a temporal distance from the early days of social restriction when the podcast was produced, this article unpacks discoveries about signal and noise made performatively within the podcast, and theorizes pandemic listening as a condition of sonic instability and attunement that opens opportunities for reflection and transformation of systemic, habitual listening practices.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.019 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 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".