THE ROLE OF GREEN SPACES IN ENHANCING RESIDENTS’ SUBJECTIVE WELL-BEING IN URBAN COMMUNITIES
Bibliographic record
Abstract
Urbanization has dramatically transformed the landscapes of cities worldwide, often resulting in a decline of natural environments and an increase in stressors that negatively impact residents' mental and emotional well-being. This paper investigates the role of green spaces—such as parks, gardens, and recreational areas—in enhancing the subjective well-being of residents in urban communities. Through a mixed-methods research design, the study combines quantitative and qualitative approaches to explore how the availability and quality of green spaces influence life satisfaction, stress reduction, and social connectivity among community members. The quantitative component involves surveys distributed across diverse urban neighborhoods, assessing subjective well-being using standardized measures and evaluating green space characteristics through Geographic Information Systems (GIS). The qualitative component includes semi-structured interviews and focus groups that provide in-depth insights into residents' experiences and perceptions of green spaces. Findings indicate that access to high-quality green areas significantly contributes to improved mental health outcomes and fosters a sense of community belonging. The paper emphasizes the necessity of integrating green spaces into urban planning and policy to promote healthier, more resilient communities. By highlighting the multifaceted benefits of green spaces, this research aims to inform sustainable urban development practices that prioritize the well-being of urban residents.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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 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".