Bibliographic record
Abstract
COVID-19 was an unprecedented global event that impacted people’s everyday lives and how cities function. To mitigate the spread of the virus, governments introduced a range of strict lockdown measures that limited non-essential activities or travel (Hale et al., 2021). These lockdown measures changed how and why people moved around their communities (FCM, 2020a). Over the spring and summer of 2020, many cities observed a decrease in automobile traffic and public transit ridership (StatsCan, 2020), while weekday cycling decreased by 10% but weekend cycling increased by 34% (Eco-Counter, 2020). In response to these changing mobility patterns, cities implemented street reallocation measures such as widening sidewalks, pop-up bike lanes, and either completely or partially closing streets to automobile traffic (FCM, 2020a). This study explores how and why Canadian cities implemented street reallocation measures, and what decision-makers can learn from this experience. City officials and active transportation advocates from ten Canadian cities participated in in-depth, semi-structured interviews to uncover street reallocation responses and motivations, decision-making processes, and lessons. Results revealed five overarching themes that led to stronger responses: evidence-informed decisions, capitalizing on existing plans and policies, drawing from internal experience, strong leadership, and collaborating with community groups. Lessons include: 1) how this experience informs future urban planning, 2) the benefits of street reallocation, 3) an opportunity to try new ideas, 4) seeing the city as adaptable, and 5) focus on equity and mobility justice. Research findings also emphasize the importance of studying the COVID-19 pandemic as a case study for emergency preparedness and structural governance changes. By drawing on these lessons, decision-makers and practitioners can ensure our cities are more resilient in the future
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.054 | 0.020 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".