Pandemic- and Future-Proofing Cities: Pedestrian-oriented Development as an Alternative Model to Transit-based Intensification Centers
Bibliographic record
Abstract
Many official smart growth-inspired Canadian plans limit sprawl by mixing land uses, transportation modes, jobs, and residents to create compact, transit-oriented, multi-functional intensification centers enriched with amenities and highly designed public spaces (Ontario Government Ministry of Municipal Affairs and Housing, 2019 [2006]; City of Toronto, 2018). However, these intensification strategies, built on new or expanded public transit systems at metropolitan, regional, and local planning scales, face challenges amid the 2020 pandemic (Filion et al, 2016). Recovery from the combined COVID-19-induced loss of commercial activity in intensification centers and confidence in public transit could take years, and combined with an increased reliance on private vehicles, could undo decades of planning efforts at shifting unsustainable land use-transportation dynamics. Concurrently, there is growing attention on sustainable cities with ample public spaces where safe walking and cycling can flourish. Advocates call for reclaiming the streets for people, pedestrians, and cyclists as a resilient strategy for cities and healthy living (Ewing, 2020a). Cities like Milan, Paris, New York, and Seattle are making permanent, temporary space accommodations to pandemic-related pedestrian flows and distancing (Laker, 2020). This chapter is based on the Canadian (and to a large extent North American) urban reality, which is dominated by low-density, functionally-specialized, and automobile-oriented land uses. Over the last decades, planning efforts to modify this urban form took the form of high-density intensification centers focused on existing or new public transit rail or BRT (bus rapid transit) systems. Such a strategy faces mounting uncertainty amid pandemic-induced, and possibly long-lasting, transit ridership, brick and mortar retailing, and office work decline.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".