COVID-19 street reallocation in mid-sized Canadian cities: socio-spatial equity patterns
Bibliographic record
Abstract
INTERVENTION: Street reallocation interventions in three Canadian mid-sized cities: Victoria (British Columbia), Kelowna (British Columbia), and Halifax (Nova Scotia) related to the COVID-19 pandemic. RESEARCH QUESTION: What street reallocation interventions were implemented, and what were the socio-spatial equity patterns? METHODS: We collected data on street reallocations (interventions that expand street space for active transportation or physical distancing) from April 1 to August 15, 2020 from websites and media. For each city, we summarized length of street reallocations (km) and described implementation strategies and communications. We assessed socio-spatial patterning of interventions by comparing differences in where interventions were implemented by area-level mobility, accessibility, and socio-demographic characteristics. RESULTS: Two themes motivated street reallocations: supporting mobility, recreation, and physical distancing in populous areas, and bolstering COVID-19 recovery for businesses. The scale of responses ranged across cities, from Halifax adding an additional 20% distance to their bicycle network to Kelowna closing only one main street section. Interventions were located in downtown cores, areas with high population density, higher use of active transportation, and close proximity to essential destinations. With respect to socio-demographics, interventions tended to be implemented in areas with fewer children and areas with fewer visible minority populations. In Victoria, the interventions were in areas with lower income populations and higher proportions of Indigenous people. CONCLUSION: In this early response phase, some cities acted swiftly even in the context of massive uncertainties. As cities move toward recovery and resilience, they should leverage early learnings as they act to create more permanent solutions that support safe and equitable mobility.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".