Bibliographic record
Abstract
Walking is undergoing a resurgence in transportation planning as a means to manage demand for single-occupant vehicles, improve public health and environmental outcomes, and recreate the vibrant human-scale communities which characterized urban life in North America until the mid-twentieth century. Pedestrian planning currently prioritizes land use and infrastructural interventions empirically proven to increase the choice to walk: dense forms which connect residents to amenities; street and intersection designs protecting pedestrians from other modes; environmental designs conducive to crime prevention; and aesthetic and functional features which offer comfort to passersby. While these features reliably predict travel behaviour for society as a whole, they fall short in explaining the propensity to walk among groups whose travel behaviour is shaped by socioeconomic constraints rather than environmental attractiveness. This epistemological gap coincides with higher rates of pedestrian injury and death among physically- and socioeconomically-disadvantaged groups across Canada. Greater attention to pedestrians' unique experiences in appraisal and decision-making could inform more effective interventions, yet improving planning practice hinges on better understanding planners' attitudes toward public consultation, municipalities' approaches toward strategic planning, and most importantly, the non-built spatial factors shaping pedestrians' behaviour and perceptions. This dissertation strives to improve pedestrian planning procedures and outcomes through four objectives: 1. To demonstrate how social space impacts pedestrians' behaviour and perceptions, and as such the ontological underpinnings of what constitutes a walkable space; 2. To illustrate how qualitative geographic information science can bring social space into walkability assessment; 3. To examine variations in professional values among transportation planners and their personal and institutional circumstances, and; 4. To assess the prevalence of social policies among strategic pedestrian plans, revealing inclusionary practices at a national scale.Findings indicate that social space impacts pedestrians' engagement with amenities and streets and, as such, that social space should be incorporated into pedestrian planning to maximize the number of opportunities that can be satisfactorily and voluntarily reached on foot by all members of society. Chapter 3 demonstrates how social distances affect pedestrians in the environmentally- and socially-heterogeneous neighbourhood of Parc-Extension, Montreal, ultimately informing a socialized walkability framework which builds upon existing frameworks informed by built characteristics. Chapter 4 illustrates how geographic information science can grapple with social spaces as perceived by residents. Chapter 5 turns toward the attitudes of transportation planners as they negotiate their professional expertise with public insights, revealing multiple types of planners whose respective views associate with institutional and training differences. Chapter 6 steps back to look at strategic pedestrian planning among Canadian municipalities, finding lacklustre public and stakeholder consultation and the neglect of certain socially-excluded groups within plan policies. Chapter 7 concludes this work by noting its contributions to knowledge and practice, as well as additional research necessary to validate and more effectively operationalize its conclusions for planning practice.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".