Greening practitioners worry about green gentrification but many don’t address it in their work
Bibliographic record
Abstract
As cities attempt to ameliorate urban green inequities, a potential challenge has emerged in the form of green gentrification. Although practitioners are central to urban greening and associated gentrification, there has yet to be an exploration of practitioner perspectives on the phenomenon. We fill this gap with an online survey of 51 urban greening practitioners in Metro Vancouver and the Greater Toronto Area. Most respondents defined green gentrification as the displacement of vulnerable residents due to the installation or improvement of green space that attracts wealthy in-movers and increases property values. They were most likely to identify greening as driving green gentrification, with a minority identifying other systemic drivers with greening in a secondary role. Although 39 of 51 participants had some familiarity with green gentrification, most reported low confidence in their understanding of the concept, little evidence of using the concept in their work, and moderate concern that their work is implicated in green gentrification. The gentrification issues most encountered by practitioners were changes to neighbourhood character and uneven investment in public infrastructure, and those working in domains linked to planning, equity, and engagement were most likely to encounter gentrification issues. Practitioners experienced multiple barriers to addressing green gentrification, including limited institutional capacity, limited access to data and relevant information, policy/mandate restriction, and lack of engagement tools. Results indicate that practitioners have a moderate understanding of green gentrification but do not often use the concept in their work, despite their potential to contribute to or exacerbate it. This suggests some resistance to critiques of urban greening practice, a failure of scholarly critiques of urban greening to influence policy change, and the need for stronger research theory and research co-creation involving practitioners and academia.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".