Grassroots mobilization for a just, green urban future: Building community infrastructure against green gentrification and displacement
Bibliographic record
Abstract
Municipal climate resiliency and re-naturing plans are promoting greening and green (re)development, such as the inclusion of new parks, greenways, or rehabilitated shorelines, frequently as a-political, win-win solutions for all residents. Greenwashing and (re)development of green amenities in vulnerable neighborhoods-those often most in need of support toward resilience and adaptation-expose residents to the impacts of green gentrification, such as the pricing-out and physical displacement from housing, socio-cultural displacement from public space, and associated personal and community traumas. This paper explores an under-researched avenue in the green gentrification literature: How do grassroots community activists organize to address housing and greening simultaneously and how do they operate to achieve justice in greening neighborhoods? We examined the strategies and tools used by community groups in 10 cities in the United States facing green gentrification. We find that justice-driven strategies and tools are supported by the formation of multi-sectoral coalitions which strengthen what we define as "community infrastructures"-social, economic, and political capacities-against exclusive green-washing. We argue that each of the three capacities must be built amongst residents in order to fortify the material and immaterial components of community infrastructure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".