No sooner said than done: A qualitative study examining equity considerations in pandemic street reallocation initiatives
Bibliographic record
Abstract
• We explore how pandemic-time street reallocations impacted equity. • Planning for transportation needs during COVID-19 introduced new equity concerns. • Successes and failures of trialed engagement methods will be considered in future. • Pre-existing plans and professional expertise helped cities make decisions quickly. The COVID-19 pandemic brought large-scale shifts in public policy, including around the use of public space. Within urban planning practice these policy shifts sparked heightened attention to equity. This paper investigates the pandemic-time street reallocations in the Canadian metropolitan regions of Vancouver, Toronto, and Montréal, with a focus on equity. In this study, street reallocations include new bike lanes, motor vehicle lane closures, and quiet streets. In 2022, we conducted semi-structured interviews with municipal practitioners who were involved in active transportation decision-making during the pandemic (Vancouver: n = 5; Toronto: n = 10; Montréal: n = 5). Participants reflected on lessons learned through street reallocation implementation two years following the initial pandemic onset. We asked about equity considerations and used framework analysis to look at common themes across the study areas. Our findings suggest that the pandemic impacted equity in active transportation planning in three main ways, by: (1) Broadening the view on equity; (2) Disrupting conventional engagement processes; and (3) Reinforcing pre-existing plans and professional expertise. Participants spoke to a changing policy landscape where traditional methods of assessing equity and conducting public engagement did not serve all population groups well. The pandemic urgency put a spotlight on the importance of communication with residents affected by infrastructure change. The pandemic also accelerated timelines of implementation, which impacted the information used to make decisions; some municipalities found pre-existing planning documents to be helpful and others relied on professional expertise. Cities are facing pressures to rethink public spaces again, in light of the climate emergency and growing social issues. As municipal practitioners decide to remove temporary street reallocations or make them permanent, this study offers insights into equity and public engagement learnings for future urban and transportation policy.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".