Temporary Urbanism in Pandemic Times—Disruption and Continuity of Public Action in Montreal
Bibliographic record
Abstract
Abstract Faced with the COVID-19 pandemic, the City of Montreal and its boroughs quickly deployed temporary facilities aimed at sharing public space and promoting active mobility (cycling and walking). This so-called strategy of “temporary urbanism” is common to North American cities from the spring of 2020. Several inventories of such measures demonstrate this. However, few of these databases open up the black box of the decision-making processes and levers that the actors have implemented to deploy this urbanism. Thus, the chapter is devoted to these processes, explaining the Montreal case in detail. It reveals the main characteristics of Montreal’s public action. As such, it highlights the local particularities of it, considered at the same time as agile, a source of numerous conflicts but also very adaptative. To conclude, we emphasize on two dimensions. First, the pandemic demonstrates that Montreal public actors had resources to respond to the crisis, rooted in action routines but also in a capacity for innovation. Secondly, that this incremental dimension of temporary urbanism is now considered by public actors as an opportunity to implement sustainable changes, in the longer term, through the deployment of a “transitory urbanism”.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".