Examining social inclusion among pedestrian plans in Canada
Bibliographic record
Abstract
Canadian policymakers promote walking to meet several goals related to transportation demand management, public health, and economic welfare. However, unequal pedestrian outcomes stubbornly persist across Canadian society. Recent debates at the intersection of social inclusion and transportation policy underscore the responsibility of stakeholders to address such inequalities and promote social engagement among excluded groups in planning procedures and their outcomes. Pedestrian plans are rare opportunities to strategize across the disparate stakeholders impacting walkable spaces—private developers, transit, parks and recreation—yet the social inclusion measures of pedestrian plans remain understudied in Canada and elsewhere. We examine pedestrian plans from 27 municipalities across the country using a social inclusion framework with participation and policy criteria. Results include that Canadian pedestrian plans fall short in promoting social inclusion with infrequent opportunities for collaborative contributions by the public; lacklustre outreach to socially‐excluded stakeholder representatives; and oversight of socioeconomic groups among accountable policies. We discuss recommendations to augment social inclusion in plan development such that socially‐excluded groups can more substantially benefit from accessible and safe walkable spaces conducive to personal well‐being and engagement with society.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.013 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".