Understanding neighbourhood change \n : a study of the street in Vancouver Downtown Eastside
Bibliographic record
Abstract
This thesis explores what effects gentrification can have on the urban environment, and how neighbourhood change is connected to and affected by global trends and local planning strategies. The analysis draws upon an empirical study carried out in a gentrifying area in Vancouver, the Downtown Eastside, which has Canada’s largest community of concentrated urban poor. The empirical material consists of data collected by the author, with an emphasis on site observations, attendance on public planning and community meetings and interviews. \n \nThe study focuses on the street and the sidewalk as public space and discusses these spaces through the lens of theory on gentrification and urban justice. \n \nThe study shows that gentrification has an impact on the street life and the physical space of the study area. The Vancouver Downtown Eastside is interpreted as a socially and economical problematic area, and the City of Vancouver attempts to carry through changes according to the concept of “revitalization without displacement”, something which this study confirms can be hard to implement successfully. Further, this thesis argues the importance for landscape architects and planners to take on an active role in creating more just and diverse cities, where segregation between socio-economic groups attempts to be avoided. Through being advocates of the urban commons and public space, where equality, diversity and processes of learning from our fellow citizens are in focus, rather than creating landscapes of consumption, this thesis argues that the profession of landscape architecture and planning can contribute to making our cities more just.
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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 | 0.002 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".