Improving the Experience for People with Mobility Issues in Urban Open Spaces in Montréal to Increase Inclusivity
Bibliographic record
Abstract
Urban open spaces play a pivotal role in the vitality of a city. It is imperative that these areas are designed to accommodate all members of the community. Among the groups deserving special consideration in urban planning are the elderly, individuals in wheelchairs, and parents with strollers, as they often encounter challenges related to mobility that hinder their access to public spaces. \nThis study endeavors to enhance the urban experience for individuals with mobility issues in Montreal's open spaces, thereby fostering greater inclusivity. The research aims to pinpoint areas of concern and overlooked aspects within these spaces, with the overarching objective of enhancing comfort and accessibility for individuals with diverse needs. \nThrough the development of prototypes and informative diagrams, this research seeks to illustrate practical solutions for mitigating barriers encountered by our target demographic in real-world scenarios. By observing public behavior in Montreal's urban spaces, we aim to provide actionable insights for urban designers, architects, and policymakers. \nUltimately, this research is poised to make significant contributions to the creation of more inclusive and accessible public spaces, catering to the needs of an aging population and promoting the well-being of all citizens.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".