Bibliographic record
Abstract
Like other major cities in North America and West Europe, Vancouver is now undergoing gentrification. In fact, housing values have continued to rise, and housing crisis has become one of the major social issues in the Canadian city. Gentrification in Vancouver is obviously and rapidly emerging in Downtown Eastside (DTES), a low-income neighborhood. In the present-day DTES, many low-income residents have been seriously threatened by the rising rents. In addition, some landlords have renovated and rebuilt their old buildings into condominiums and shops for the middle class, resulting in a decrease in affordable houses in this area and the eviction of tenants. However, some local groups and activists have declared a housing crisis and decried these developments to be a violation of the human rights of the low-income people in this neighborhood, demanding an increase of social housing. Furthermore, another phenomenon in DTES related to gentrification is that some local organizations and social enterprises have been trying to develop and support local businesses and create jobs in the community; however, this effort has sometimes conflicted with agenda of activists working on housing issues. This paper explains these two kinds of opposing movements concerning gentrification in DTES and their impacts on the community. It also examines the discussions on two locally oriented movements against gentrification to offer suggestions about the interaction between the advance of neoliberalism in global cities and the survival of local communities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".