(Mis-)belonging to the climate-resilient city: Making place in multi-risk communities of racialized urban America
Bibliographic record
Abstract
Through climate adaptation planning cities are transforming places and relations, most recently via green climate resilient infrastructure (GRI). Yet, GRI's incorporation into existing, racialized infrastructure systems of urban development, regeneration and finance has raised questions about the socio-cultural impacts and justice dimensions of recent directions in climate adaptation planning and urbanism. While critical scholars highlight the exclusion of historically marginalized residents, this paper's analysis of the impacts of GRI-driven planning for sense of belonging reveals a complex and multi-faceted experience of gentrification and displacement in the racialized, settler colonial city. Drawing on insights from civic actors about their lived experience of green and climate resilient projects in Boston, Massachusetts, we develop a novel understanding of belonging, which entails degrees of (mis)belonging. Our analysis uncovers three pathways by which climate urbanism shapes belonging into various alienated, subordinated, assimilated and emancipated forms, and reveals the kinds of political subjects and socio-cultural relations that emerge from the lived experience of climate adaptation projects. More broadly, this study sheds light on how less visible placemaking practices and alternative modes of addressing socio-climate vulnerability contribute to climate justice and injustice dynamics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.007 |
| 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".