The Spatial Dimensions of Social Capital: Examining the Relation between Built Environment, Communities, and Policy in Canadian and Peruvian Cities
Bibliographic record
Abstract
This dissertation proposes an integrated framework to study the impact of the built environment of cities and neighbourhoods on social capital. Drawing on three case studies in Canadian and Peruvian cities, I offer insights into how citizens and policymakers impact the urban landscape and communities’ social milieu. My three guiding questions interrogate (1) the spatial patterns of social capital, (2) the effect of urban elements (e.g., street design, social infrastructure, policy, among others) on communities’ social capital and trajectories, and (3) the role of decision makers (e.g., neighbourhood associations and local authorities) in shaping those urban elements. My approach is centred around the effects of three spatial concepts—density, infrastructure, and design—on neighbourhoods through spatial mechanisms. Specifically, I explore (1) the spatial configuration of social capital, (2) the bounding of social capital through built barriers to mobility (micro-segregation), and (3) the imposition of categorical boundaries via spatially targeted policy. First, I propose that the configuration of cities influences social trust. Amenity rich areas with pedestrian-friendly design create more opportunities for informal interactions, which in turn promotes trust. I elaborate this hypothesis studying Canada’s five largest metropolitan areas: Toronto, Montreal, Vancouver, Ottawa-Gatineau, and Edmonton. The second mechanism suggests that exposure to segregating infrastructure bounds social capital—it becomes more inwardly oriented. Using the case of Lima, Peru, I hypothesize that micro-segregating infrastructure (fences, gates, and walls) bounds social capital across social groups. Finally, I suggest that geographically targeted policy interventions impose social boundaries. Programs designed to strengthen deprived neighbourhoods’ social capital may instead create stigma towards them by categorizing them as “poor” or “in need.” I test this hypothesis using Toronto’s Priority Area Program. In the conclusion, I outline a theory of spatiality and social capital and the opportunities for future research that it opens.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".