Bibliographic record
Abstract
In cities across the world, both in the global South and North, the distribution of urban green spaces exhibits stark inequalities. Affluent neighborhoods are often graced with abundant, well-maintained parks and green areas, offering residents a higher quality of life and environmental benefits. In contrast, communities with lower incomes and minority populations frequently face a scarcity of such spaces, and the green areas they do have tend to be of lower quality. This disparity not only reflects broader social and economic inequities but also has significant implications for public health, environmental justice, and social cohesion. Efforts to rectify this imbalance, while well-intentioned, can inadvertently lead to gentrification. Improving green spaces in underserved neighborhoods often makes these areas more attractive to higher-income groups, driving up property values and living costs. This process can displace long-term, lower-income residents, ironically exacerbating the very inequalities such initiatives aim to address. The resulting gentrification can also lead to increased homelessness among the most vulnerable populations. Thus, urban planners and policymakers face a complex, paradoxical challenge: how to equitably enhance urban greenery without contributing to gentrification and the further marginalization of low-income communities. This dilemma underscores the need for inclusive, carefully considered strategies in urban environmental planning that prioritize the needs and voices of all residents, especially those in historically marginalized communities.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".