Case study: A survey of perceived noise in Canadian multi-unit residential buildings to study long-term implications for widespread teleworking
Bibliographic record
Abstract
Trends of urbanization, densification, and telework all point to increasing exposure to ambient noise for workers. With the lockdown policies implemented in response to COVID-19, a research opportunity to study perceived noise exposure for teleworking arose. This paper presents the results of a survey on noise issues in multi-unit residential buildings (MURBs) and the consequent effects on occupants' well-being and productivity during the lockdown. Responses were collected from 471 MURB occupants across Canada. The results show that, despite the decrease in environmental noise, many are annoyed by outdoor noise, particularly from traffic and construction activities, and indicated that it affects their ability to work. Effects on ability to work from home were more frequently reported for indoor noise sources particularly airborne and impact noises coming from neighboring suites. Our findings, however, show that noise coming from occupants in the same suite (i.e. roommates and family) present the biggest issue. The findings indicate that existing noise conditions in MURBs might not be suitable for a permanent large-scale implementation of teleworking.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".