There goes the neighbourhood: a case study of social mix in Vancouver's downtown eastside
Bibliographic record
Abstract
Social mix is a highly contested global trend in urban planning as it can result in some of the same negative social consequences as gentrification, such as displacement and social polarization. In 2014, the City of Vancouver approved a social mix strategy for one of its low-income neighbourhoods in their Downtown Eastside (DTES) Local Area Plan (LAP). With this plan, the city aimed to increase mid- and high-income residents in a predominately low-income neighbourhood. Included were Social Impact Objectives to mitigate harm to existing low-income residents, and assurances the approach would benefit all community members. The LAP provoked questions of whether social mix could, indeed, benefit low-income residents. This qualitative single-case research study investigates the experiences of residents with low incomes in the DTES neighbourhood, three years after the implementation of the LAP. The study is grounded in an anti-oppressive framework, with attention to anti-colonization and the unique experiences at the intersection of gender and colonial oppression. Three key findings emerged from neighbourhood observations and semi-structured focus groups conducted in 2017 with twenty-four research participants. First, experiences of displacement in the DTES were reported; second, experiences of social polarization within their neighbourhood were described; and, third, most participants demonstrated strong community connections despite the social mix changes. The findings suggest low-income residents did not benefit from social mix and, if further displacement and polarization were to continue, the negative impact on low-income residents would increase.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".