Transnational Experiences of COVID-19: Transferable Lessons for Urban Planning Between the Global South and the Global North
Bibliographic record
Abstract
COVID-19 reveals that the equity dimension of planning, especially concerning the distribution of infrastructure, services, and amenities, is integral to urban resilience. While neoliberal approaches to economic and social life over the past 40 years have challenged this idea, the pandemic has helped planners to reassert the need for greater income equality and re-establish the effectiveness of coordinated and collectivized responses to disruptions. This chapter explores these issues by focusing on transferable lessons from the experiences of Aalborg, Colombo, Melbourne, and Toronto, and on how urban planners can help address a range of inequalities and inadequacies aggravated by the pandemic. These cities were selected to demonstrate how city size, political institutions, and level of economic development, along with location in different parts of the world, affected reactions to the pandemic. In this chapter, we review the experience of lockdown and phased reopening in each city-region over the first six months after the declaration of the pandemic in March 2020. We then consider the role of governance mechanisms and capacity in response to managing the devastating impacts of a public health crisis and associated economic, social, and spatial outcomes. By drawing upon examples from each city-region on resilient planning practice, we conclude by highlighting opportunities for mutual international learning in connection to pandemic and post-pandemic urban planning. The four cities (and respective countries) experienced the pandemic and subsequent reopening of their economy in both shared and unique ways. Despite differences in overall population, density, and profile, all four territories recorded relatively low case fatalities and deaths as a percentage of population over the first wave of the pandemic (Table 14.1).
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.018 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".