Quality over Quantity: Contribution of Urban Green Space to Neighborhood Satisfaction
Bibliographic record
Abstract
There is increasing evidence that the quality of green space significantly contributes to neighborhood satisfaction and well-being, independent of the mere amount of green space. In this paper, we examined residents’ perceptions of the quality and beneficial affordances of green space in relation to objectively assessed accessibility and usability. We used data from a survey in two neighborhoods (N = 223) of a medium-sized city in the Netherlands, which were similar in the amount of green space and other physical and socio-demographic characteristics, but differed in the availability of accessible and usable green spaces. Results show that residents of the neighborhood with a higher availability of accessible and usable green spaces were more satisfied with their neighborhood. This difference was statistically mediated by the higher level of perceived green space quality. Neighborhood satisfaction was significantly positively related to well-being. However, residents of the two neighborhoods did not differ in self-reported well-being and beneficial affordances of green space. These analyses contribute to a further understanding of how the accessibility and usability of green spaces may increase people’s neighborhood satisfaction. It highlights the importance of perceived quality in addition to the amount of green space when examining the beneficial effects of green space.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".