<scp>RACIAL INEQUITY IN GREEN INFRASTRUCTURE AND GENTRIFICATION</scp>: Challenging Compounded Environmental Racisms in the Green City
Bibliographic record
Abstract
Abstract This article explores the role that green gentrification plays in exacerbating racial tensions within historically marginalized urban communities benefiting from new environmental amenities such as parks, gardens, waterfront restoration and greenways. Building on extensive qualitative data from three cities in Europe (Amsterdam, Vienna, Lyon) and four cities in the United States (Washington, Austin, Atlanta, Cleveland), we use thematic analysis and grounded theory to examine the complex relationship between historical environmental and racial injustices and current racial green inequities produced by the green city agenda. Our analysis also offers insights into the main differences in how community members articulate concerns and demands over racial issues related to green gentrification in Europe versus North America. Results show that urban greening—and green gentrification specifically—can create ‘compounded environmental racisms’ by worsening racial environmental injustices and further perpetrating green racialized displacement, re‐segregation and exclusion. The latter is produced by the racial inequities embedded in green infrastructure projects and the related unequal access to environmental benefits, affordable housing, political rights and place‐making. Moreover, we find that settler colonial practices combined with persisting exposure to toxins and re‐segregation in the United States together with neocolonial spatial and social practices in Europe shape how racialized community members perceive and interact with new green amenities.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 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".