How common is greening in gentrifying areas?
Bibliographic record
Abstract
Green gentrification occurs when urban greening/sustainability interventions become implicated in neighbourhood upgrading and displacement of existing residents. However, current emphasis on urban sustainability in planning/policy agendas, coupled with political-economic factors producing uneven development, lead us to ask whether <i>all</i> gentrifying areas experience greening. Our descriptive analysis identified gentrifying areas in Vancouver, Calgary, and Toronto (Canada), from 1996–2006 and 2006–2016, and determined the extent to which various greening interventions (parks, cycle lanes, community gardens, LEED-certified buildings, and rapid-rail transit) were introduced before, during, and after gentrification. Greening frequently occurred before and/or during, and after, gentrification. Our results indicate greening is common in gentrifying areas throughout the gentrification process, suggesting the need for a broader understanding of the relationship(s) between urban greening and gentrification. We outline a future research agenda to examine greening across gentrifying areas and further understand how these two processes shape each other in the remaking of neighborhoods/cities.
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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) | 0.016 | 0.001 |
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".