Improvement, not displacement: A framework for urban green gentrification research and practice
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 researchers have continued to expand the bounds of green gentrification scholarship, understanding of what green gentrification is and how to identify the phenomenon on the ground has grown obscured. In an attempt to bring clarity to this conversation, our research presents an urban green gentrification framework, based on findings from a scoping review and dimensional analysis conducted across green gentrification, urban greening, and related literatures. Our study is guided by two primary objectives: (1) identify the key dimensions of green gentrification as it pertains to urban greening; and (2) explore the relationships and intersections between dimensions in terms of their implications for the social impacts and outcomes of urban greening initiatives. We identify three principle dimensions of green gentrification as it relates to urban greening — conceptual foundations; design and intent; and socio-spatial change — as well as six related sub-dimensions. Considered in tandem, these dimensions present green gentrification as a dynamic process bound within a history of exploitative, neoliberal social and economic processes, operating beyond the scope of any single urban greening initiative. Responding to green gentrification, therefore, requires trans-dimensional strategies that consider these structural influences guiding patterns of urban greening investment and development. Our results also show that green gentrification is not a sufficient explanation for the complexities of urban development and change, and greening should be considered alongside other drivers of gentrification more broadly.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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