Perceived urban ecosystem services and disservices in gentrifying neighborhoods: Contrasting views between community members and state informants
Bibliographic record
Abstract
As assessing urban ecosystem services and disservices is of rapidly growing interest in a context of increasingly urbanized environments, greater scholarly attention needs to be placed on how different informants perceive these services and disservices. Previous research in urban geography and planning has already pointed at the challenges of building inclusive natural outdoor environments such as green and blue spaces in gentrifying neighborhoods, particularly those undergoing green gentrification. In response, we analyze the ecosystem services and disservices identified by community and state respondents in seven cities with gentrifying neighborhoods, pronounced social inequalities, and where natural outdoor environments were created or improved: Amsterdam, Bristol, Cleveland, Lyon, Montreal, Philadelphia, and San Francisco. We found that in cities experiencing green gentrification, interviewees – particularly community informants – reported a wide array of ecosystem services and disservices, and identified some disservices previously under-studied (i.e. physical tiredness, low attractiveness and forced displacement). Our study illustrates how differences in decision making positions can impact perceptions of ecosystem services and disservices. Our study has implications for urban environmental planning decisions that will help maximize the ecosystem services provided by urban natural outdoor environments. Only if all perceived ecosystem services and disservices are considered, will it be possible to design green just cities.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".