From greens to safety: Exploring the relationship between outdoor neighborhood conditions, neighborly bonds, and social integration among public housing residents
Bibliographic record
Abstract
This study aims to examine the relationship between outdoor environmental quality, trust in neighbors, and social integration among residents of public rental housing in Seoul, South Korea. It also investigates how neighborly relationships moderate this association. The study uses data from the 2016-2021 Seoul Public Rental Housing Panel Survey (SPRHPS) and applies individual fixed effects models to examine how outdoor environmental quality affects trust in neighbors. It also explores the moderating role of dwelling type characterized by apartment and non-apartment (e.g., single detached house and multiunit housing). Poor outdoor environmental quality, characterized by low greenery, inadequate amenities, and unsafe conditions, was linked to diminished trust in neighbors. Interestingly, the negative impact of these outdoor conditions on neighborly trust was less pronounced among residents of apartments compared to those living in non-apartment settings. This trend was also observed in other outcomes, such as attitudes toward social integration. These findings highlight the importance of both physical environmental improvements and fostering social connections in public housing communities to enhance social trust and overall well-being.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".