The relative importance of greening in attracting gentrifiers to urban Vancouver and suburban Calgary neighbourhoods
Bibliographic record
Abstract
Green gentrification describes how greening neighbourhoods (e.g. by creating parks, community gardens, etc.) can result in higher-income households moving in and displacing/excluding marginalised residents. While some researchers assert that greening attracts higher-income households, this has rarely been empirically tested. Further, green gentrification research has focussed almost exclusively on greening attracting households to urban neighbourhoods, despite desire for more green space often being cited as motivating households to move to suburbs . Our study surveyed 104 households in gentrified downtown Vancouver and suburban Calgary neighbourhoods, to determine the relative importance of neighbourhood greenness and proximity to green space when they were deciding to move into new-build neighbourhoods. Our results indicate that green factors are of similar importance to non-green factors, such as safety, scenic views, ambience and, in Vancouver, proximity to entertainment and transit. Proximity to green space was more important than overall neighbourhood greenness. Residents in all neighbourhoods placed similar importance on green factors, although more importance was placed on private green space in the suburbs. These findings suggest that neighbourhood greenness and proximity to green space are not the only factors driving high-income households to move in and that green factors have played a similar role in motivating households to move to urban and suburban neighbourhoods. Thus, green-gentrification research needs to consider how preference for greened neighbourhoods intersects with other preferences/constraints to ultimately influence residential location choices. It also needs to widen the geography of green gentrification to understand how greening contributes to exclusion and displacement beyond dense city environments.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".