Greening the gentrification process: Insights and engagements from practitioners
Bibliographic record
Abstract
Green gentrification implicates urban greening as a driver of neighbourhood ‘upgrading’ and subsequent displacement. However, it is unclear whether the concept resonates with, or supports the work of, those responsible for much of the greening occurring in cities – urban green planners/practitioners. We interviewed 33 planners/practitioners in Canada to refine our understanding of the relationships between urban greening and gentrification. We found that greening is closely tied to development, with funding/space for greening often provided through development requirements/incentives. Thus, rather than greening causing gentrification (as described in current literature), here greening is often a requirement and direct outcome of new development – contributing to what we describe as a broader greening of the gentrification process that is facilitated by various political-economic factors. Many interviewees stated that their current work focuses on addressing existing inequities rather than strategizing to limit future gentrification. However, they had mixed opinions about whether knowledge of green gentrification as a concept can help them promote equitable urban greening due to their lack of power over where/how urban greening occurs, along with the finding that greening is not causing gentrification. The uneven power dynamics between urban green practitioners/planners, developers, and elected officials also influenced views on whether gentrification is an intended outcome of greening. We conclude that relying on new development to provide urban greening is antithetical to addressing existing green inequities and is likely to exacerbate inequities through associating greening with gentrification. Recent measures to improve housing affordability (i.e. the removal of developer greening requirements) will disrupt the current development-greening relationship but are unlikely to address the issue of inequitable greening. Increased and ongoing collaboration between those working in urban greening, housing, and planning is paramount and should focus on affordability and equity across urban systems – attending to the interplay between greening, housing, affordability, and sustainability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".