“They Didn’t See It Coming”: Green Resilience Planning and Vulnerability to Future Climate Gentrification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As cities strive to protect vulnerable residents from climate risks and impacts, recent studies have identified a challenging link between these measures and gentrification processes that reconfigure, but do not necessarily eliminate, climate insecurities. Green resilient infrastructure (GRI) may especially increase the vulnerability of lower income communities of color to gentrification, an issue that remains underexplored. Drawing on the forerunner green city of Philadelphia, Pennsylvania, as our case study, this article adopts a novel intersectional approach to assess overlapping and interdependent factors in generating vulnerability and resilience using spatial quantitative data and qualitative interviews with community-based organizers, nonprofits, and municipal stakeholders. More specifically, this article develops a new methodology to assess vulnerability to future climate gentrification and contributes to debates on the role of urban development, housing, and sustainability practices in climate justice dynamics. It also informs strategies that can reduce social and racial inequities in the context of climate adaptation planning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it