Bibliographic record
Abstract
Abstract The Covid-19 pandemic catalysed disruptions and disturbances in ways of living across the globe. Many of these changes in daily life were felt through stark changes to our soundscapes, particularly those in urban centres. Might we better understand the effects of the Covid-19 lockdowns through sonic analysis? This chapter explores how sound analysis methods, including concepts of the sound-motion object and sonic image, might aid in understanding the environmental soundscapes of the pandemic lockdowns. The discussion focuses on the Sounding Covid-19 project—an initiative involving a series of field recordings carried out during Covid-19 pandemic-related events in the urban environments of Belfast, Northern Ireland (2020–2022) and Montreal, Canada (2020–2021). The project presents the sound archive through various listening experiences, including soundscape compositions, sound mapping and narrative-based radiophonic work. We consider how the pandemic may have invited us to pause and reconsider how we document and archive the present to look back and better understand the future. Sound may be vital in understanding our environment and the socio-cultural shifts over time. This chapter argues that documenting, preserving, and analysing the soundscapes of the pandemic lockdowns may help us reflect on our shared histories in several ways.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".