Pedestrianizing strategies and street liveliness: a case study of Montreal (Canada)
Bibliographic record
Abstract
Pedestrianization has become one of the strategies privileged, especially during the COVID-19 pandemic, to handle social distancing measures and boost the local economy. Yet there is little knowledge about street design impacts people’s usage as well as how pedestrianization is conceived by local governments. In this paper, we examine two pedestrianized streets in Montreal (Canada), having different goals and design. In summer 2021, we conducted a systematic observation of street users and a review of policy, design, and press documents. In both streets, we found design important in explaining the presence of large groups in private consumption spaces and small groups in segments with rich public furniture. Local life explained street usage of homeless people and seniors, while cultural activities increased large groups. We underline the preponderance of economy-oriented goals in the municipality’s strategies which favour crowdedness but potentially undermine social functions of streets. We call for more attention to the public realm in and around pedestrian streets in order to make space for vulnerable populations and to enhance the sense of community. As such we hope to contribute to an inclusive streetscape reallocation and inform practitioners in creating lively public spaces.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".