Noise impacts and social justice analysis of off-peak deliveries in the Greater Toronto Area
Bibliographic record
Abstract
This study investigates noise impacts of Province of Ontario legislation that permitted off-peak deliveries (OPD) in the Greater Toronto Area (GTA), Canada, which was initiated at the beginning of the pandemic lockdown in March 2020. The study presents an analysis of noise complaints, results of a community noise survey of residents living near retail stores that received deliveries during evening and night-time hours, and analysis of racial and income disparities in noise impacts. 0.76 % of total noise complaints in Toronto are found to be due to off-peak commercial deliveries, indicating that OPD are a small but non-negligible portion of the noise experienced by residents. The community noise survey gauged noise perception by residents before and after the onset of the pandemic, when OPD began. Noise from ‘nearby business establishments’ reduced for most residents during the pandemic. Ratings of noise levels at all times of day decreased since the pandemic began, except for night-time, which increased for a small number of residents both within and outside of 150 m of a known OPD site. Only 7.2 % of respondents within 150 m of a known site of OPD ‘always’ heard evening/night-time truck deliveries to nearby businesses. Out of ten common noise sources presented to respondents, evening/night-time truck deliveries to nearby business establishments were the seventh most frequently heard noise for those living near known sites of OPD, and the least often heard for those living beyond 150 m from known sites of OPD. We do not find significant racial or income disparity in perception of evening/night-time truck deliveries noise. • A noise survey is conducted to study the noise impacts of the OPD program. • On average, off-peak truck deliveries are heard less often than day-time deliveries. • No significant racial/income disparity is found in noise perception due to the OPD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".