Adaptive governance, hybrid temporary urbanism, and outdoor spaces: Post-pandemic legacies in New York and Toronto
Bibliographic record
Abstract
This paper reflects on the development and evolution of hybrid forms of temporary urbanism, as well as the post-pandemic legacies of adaptive governance. Informed by 34 interviews with municipal, community, and business association leaders, it contributes to debates about emergency urbanism and the politics and governance of public health associated with the adaptation of streets and sidewalks in New York City and Toronto. We find that the initial, reactive adaptations of outdoor spaces occurred because of a hybrid form of adaptive governance, favoring both bottom-up and top-down collaborations between weakened governments and strong, established community organizations. In examining the legacy of such initiatives, we demonstrate that rapid, adaptive governance was not sustained. In conclusion, the paper examines how government agencies can better prepare for future crises. We suggest that the most important elements are not the specific plans for an inherently uncertain future, but rather the ability to mobilize diverse and flexible resources and, more importantly, to address lock-ins through a combination of agile strategies that display both strong and weak forms of governance. This, in turn, requires trust and a more devolved, place-based distribution of power in urban-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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".