Top pedestrian concerns in Canada mapped on WalkRollMap.org
Bibliographic record
Abstract
Abstract Walking is a healthy, sustainable, and economical form of transportation or recreation. Yet in North America walking is not always accessible, safe, or comfortable. A challenge to creating quality pedestrian environments is lack of data on what barriers exist and how barriers vary across communities. Our goal is to characterize pedestrian barriers and concerns at the microscale level. We analyzed 2,588 reports of hazards, missing amenities, or incidents from WalkRollMap.org , a crowdsourced webmap of barriers to walking or rolling. We assigned themes related to actionable infrastructure interventions and summarized data by location, walkability, street type, and characteristics of who reported it (age, gender, and self‐reported disability). Most reports were related to crossings (45%), sidewalk quality (29%), and the volume and speed of cars (13%). Reports were more common in more walkable places (likely related to exposure) and on major roads. People living with a disability reported sidewalk concerns at a higher rate than others, while people over 75 years of age were more likely to identify issues related to the volume and speed of cars. Cities should prioritize risk reduction interventions for pedestrian road crossings and sidewalk improvements, especially on major roads in amenity dense walkable places.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".