A Causal Analysis of the Sense of Community for High-rise Residents in Bangkok Metropolitan Area
Bibliographic record
Abstract
This multidisciplinary research focuses on investigating the environmental-psychological causality of the sense of community among high-rise housing's residents. The cross-sectional survey had been conducted in six different zones of Bangkok metropolitan area by employing the multi-stage sampling technique. Correspondingly, the 1,206 participants living in eighteen residential high-rises responded to the personal and environmental psychological (PEP) questionnaire, whereas, the physical conditions of the buildings were examined and evaluated by utilizing a non-participant observation along with the physical environmental (PE) assessment. The multiple linear regression analysis was a major approach applied for analyzing and endorsing the causal effects of the independent variables on the sense of community of the respondents, which was measured in a rating-scale type. The set of independent variables were classified into five categories, namely, (a) urban and community factors, (b) architectural factors, (c) personal attributes and dwelling behavioral factors, (d) personal psychological factors, and (e) environmental-psychological factors. The predictive model identified ten determinants that significantly dominated the variance of the sense of community at the 95% confidence interval (significance level of .05). Regarding the final regression equation, it revealed that the communal character of the building, social capital and participation, mental health condition, relationship with neighbors, and privacy satisfaction were the factors that enhanced the high-rise residents' sense of community. On the contrary, population density, the defensible character of the building, the privacy-supportive character of the building, introvert personality of the residents, and the average of safety concern were the factors that negatively influence their sense of community.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".