Assessing the link(s) between urban greening and gentrification in Canadian cities
Bibliographic record
Abstract
Urban greening (i.e., increasing vegetation or other sustainability initiatives) has been central to efforts to improve urban sustainability and livability. However, it has been linked to negative social impacts through green gentrification, in which greening results in neighbourhood ‘upgrading’ and the displacement/exclusion of vulnerable/marginalized residents. To date, green gentrification research has centred around the notion of greening causing gentrification, and it has infrequently considered how greening intersects with non-green factors during the gentrification process. This dissertation takes a critical approach to refine our understanding of the relationship(s) between urban greening and gentrification. It does so by using a systematic literature review, spatiotemporal analyses, interviews with urban greening planners/practitioners, and surveys with residents of greened and gentrified neighbourhoods to: i) identify gaps in existing green gentrification literature; ii) determine whether urban greening has been common in gentrifying areas, and if so, why; and iii) identify how and why urban greening becomes connected to gentrification in Canadian cities. I found that the green gentrification literature provides little empirical evidence that greening alone causes gentrification by attracting gentrifiers to move in, despite some researchers defining the process this way (Chapter 2). By surveying residents in gentrified neighbourhoods, I established that green features were one of several important factors motivating households to move into both urban and suburban neighbourhoods (Chapter 5). My spatiotemporal analyses indicated that across all gentrifying areas, greening occurred very commonly before, during, and/or after gentrification, suggesting greening has become a common feature of the process (Chapter 3). Further supporting this, my interviews with urban green planners/practitioners highlighted that much of the funding/space for new greening comes from requirements/incentives levied on new developments (Chapter 4). This results in much of this greening being located adjacent to developments targeted towards higher-income households. Overall, the results of this dissertation suggest that greening is not always causing gentrification, but the two processes frequently become intertwined in current approaches to urban greening and development in Canada. This upholds inequitable distributions of vegetation and patterns of uneven development. These findings indicate the need to centre equity and justice in planning and policy across entire urban systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".