COVID Street Cafés: Assessing Policy Windows in Five North American Cities
Bibliographic record
Abstract
Problem, research strategy, and findings: Cities experimented with street design to address health and economic impacts of the COVID pandemic between 2020 and 2023. Scholars have suggested that COVID represented a window of opportunity to transform aspects of urban planning, but the degree to which emergency interventions portend longer-term changes requires study. This study focused on street cafés as one of the more significant COVID-era street transformations. Street cafés are allocations of space in the cartway for outdoor dining. They introduce a social use to street space while increasing the complexity of managing street space. To assess the idea that the COVID crisis was a window of opportunity, we conducted a comparative case study of COVID street café programs in five North American cities. We drew on 16 interviews and analysis of policy documents to examine how street café programs were implemented, how they adapted existing policy and institutions, and whether their implementation represented a policy punctuation. Despite common objectives, implementing COVID street café programs played out differently in each case city. Urban form, existing policy, administration of emergency programs, and café design and experimentation all affected outcomes. These COVID-era programs led to one city initiating a new street café program; in two other cities, they accelerated implementation of recent policy initiatives, and the experience in the final two cities was ambiguous. Takeaway for practice: This research highlights the importance of recently adopted street policy in shaping emergency response. Although installation of COVID street cafés was widespread, planners will have to renegotiate emergency actions taken to lessen regulatory, administrative, and financial hurdles to café implementation to sustain viable street café programs in the long run.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".