Reaching the Unreachable: Social Planning in Vancouver's Downtown Eastside and Winnipeg's North End, Canada
Bibliographic record
Abstract
Social planning has remained a longstanding element of urban planning practice and continues to be pursued through different institutional structures in Canada. While the City of Winnipeg currently features no municipal social planning department, the City of Vancouver attempts to support its most disadvantaged neighbourhoods through its municipal sector. As these are two cities with high concentrations of Indigenous populations, this research uncovers the degree to which these two social planning models have worked to support the particular needs and interests of residents living in Winnipeg’s North End and Vancouver’s Downtown Eastside. Through the comparative case study of these two research sites, comprehensive document analysis and semi-structured interviews with key informants from planning agencies in each city, it is evident both models promote socially just and socially sustainable planning processes and outcomes within their respective neighbourhoods. However, neither is without fault. As a government body Vancouver is able to create and track progress in a more systemic way, setting targets and metrics for other government agencies, while information sharing and relationship building are where non-profit organizations in Winnipeg truly excel. This research explains how most non-profit organizations are unable to successfully sustain themselves, while municipal departments lack the rapport grassroots organizations more easily attain. Therefore, an integration of both models could begin to better support Canada’s most disadvantaged neighbourhoods with growing urban Indigenous populations.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".