Green gentrification & the luxury effect: uniting isolated ideas towards just cities for people & nature
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
The growth of cities creates challenges to biodiversity and social justice. Researchers addressing these challenges often do so in silos; their efforts persistently separate justice for non-humans and humans. We analyze this separation through two concepts—luxury effect and green gentrification—that each explores urban greenspace and justice, but from different angles. The luxury effect implicitly targets justice for non-humans by exploring biodiversity in cities and finds that wealth explains the distribution of biodiversity. Green gentrification explicitly targets justice for humans by examining how new greenspaces affect people and finds that such greenspaces often displace vulnerable people. Using scientometric analyses, we show the disjunction between these concepts. We draw on socio-ecological justice concepts to suggest that disregarding relationships and conflicts among humans and non-humans in concepts does not eliminate them in practice, but stalls progress toward just cities. We urge scholars to simultaneously focus on justice for humans and non-humans.
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 | 0.000 |
| 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) | 0.001 | 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