Revisiting Public Space Transformations from a Sonic Perspective during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic has changed how people relate to and use outdoor spaces, particularly in densely populated areas. We investigate the transformations that took place during the 2020 lockdown and the first post-lockdown summer, with an emphasis on changes to the sound environment, in the context of a mixed-use central neighbourhood in Montreal (Plateau-Mont-Royal), Canada. Semi-structured interviews with thirteen residents, conducted in autumn 2020, showed how restrictions on the use of indoor spaces, including a ban on indoor gatherings, coupled with the transformation of home environments into work, study, and relaxation spaces drove Montreal residents to engage more with outdoor public spaces in their neighbourhoods. This resulted in extended uses in terms of area, activities, duration of stay and even time of use, and in new uses for activities once restricted to indoor spaces (e.g. family meals, celebrations). Sound played a critical role in these public space transformations, as the diversity of uses and activities brought back the sounds of human activity and even encouraged a sense of 'normality': a safe and shared form of coming together that had been lost following the COVID-19 lockdown. The study highlighted the diverse, extended roles that (outdoor) public spaces can play in everyday urban life, beyond just providing access to quiet and the sonic consequences of this use in reinforcing previously paused forms of public life. Furthermore, intentional forms of transformations of spaces, like pedestrianizations, offer flexible amenities, impromptu musical performances and organized socializing space and ful filled roles previously satisfied by third places and effectively became temporary 'fourth places'. These findings provide grounds for reimagining the future of public spaces – not only in urban practice but also in the social imaginary, especially in relation to temporary interventions and programming, as well as promoting positive sound outcomes in public spaces.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".