Bibliographic record
Abstract
Introduction Cities around the world have rushed to respond to the COVID-19 pandemic by regulating public space to promote social distancing and stimulate economic recovery. The resulting decisions are what we term ‘pandemic pop-ups’ – hasty, real-time, and temporary changes to the use and regulation of public space. Focusing on Toronto, Canada, and Sydney, Australia, we argue that pandemic pop-ups extend beyond immediate infrastructure needs to how cities govern generally. Pop-ups may replace cars with bikes or extend restaurants into streets, and for this they have been celebrated: for saving jobs, and for making streets safer and more enjoyable. Pandemic pop-ups are not universally positive, however. They also remove tent encampments, make racialized residents more vulnerable to sanctions, and rush through controversial infrastructure projects. As we consider pandemic and post-pandemic cities, the governance of pop-ups demands critical scrutiny. The laws that regulate urban space are always open to multiple interpretations (Cover, 1983). The force of law depends on its social context, on the ability of legal actors to give effect to their preferred interpretations and the lack (or inability) of others to challenge those interpretations. Through pop-ups, cities enact a particular form of legality – by which we mean not just legal texts, but the range of rules, practices, and understandings through which those texts take effect in the world – that weakens democratic oversight and participatory processes. With an emphasis on speed over process, pop-ups have invariably been deployed without oversight or engagement, and rarely involving the voices of racialized or vulnerable people. We recognize the value that pop-ups can bring to cities – socially, economically, and environmentally – as well as the urgent challenges that make pandemic pop-ups critical. In this chapter, however, we focus on more troubling aspects that have often been overlooked. To do this we challenge two features that are conventionally associated with popups: their irregularity and their scope. First, most accounts describe pop-up planning as exceptional, a deviation from usual practices of decision-making. Yet in the time of COVID-19, pop-ups are the ‘new normal’. Second, we argue that pop-up infrastructure is broader than previously acknowledged, extending beyond bike lanes and patios to homeless encampments and policy proposals. Since pandemic pop-ups re-shape public space and the regulations through which it is governed, decisions must be made within a framework of inclusive and participatory decision-making.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.066 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".