The Future of Streets in an Age of Pandemics
Bibliographic record
Abstract
There is not a place unaffected by the Covid-19 pandemic. In response to the pandemic, with its recommended public health social distancing guidelines of six feet, city transportation agencies have repurposed street space for residents to safely travel and recreate outside. At the same time, transportation agencies have become essential in partnering with local businesses in their expansion of dining space into public right-of-way space: sidewalks, parking lanes, and vehicular lanes. City agencies have had to adapt, evolve, and respond quickly to the current pandemic in order to effectively provide residents and businesses the ability to safely go outside and to continue some level of business.\nThe work presented in this thesis includes a quantitative and qualitative analysis of city transportation agency responses to Covid-19. San Francisco, Portland, Seattle, and Toronto serve as case study cities. Interviews were conducted with relevant city personnel from each city in order to gain a nuanced and detailed understanding of how cities are responding, what factors instigated responses, how project logistics differ under a pandemic, and how vulnerable populations were supported by these responses.\nThe researcher found that all cities studied had a prior inclination to people-friendly projects, that approval and outreach processes were bypassed in order to respond quickly to Covid-19, that certain projects will become permanent, and others have the potential to do so, and that project success is often context and locality specific. The equity maps demonstrate that there is much more work to be done to support vulnerable populations.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.018 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".