Towards Better Social Housing Policy Based on Residents’ Satisfaction: A Case Study at Sendangmulyo, Semarang, Indonesia
Bibliographic record
Abstract
Sendangmulyo housing is one of the social housings for middle-income community in Semarang City built by the Indonesian National Housing Authority (PERUMNAS) in 1994. It was built based on the PERUMNAS social housing policy to provide houses for middle-income communities with affordable prices. However, Sendangmulyo inhabitants have limited fund for preservation of the environment. The limitation has caused environmental degradation, i.e., damage roads in several locations, poorly maintained drainage, poor water quality, and inadequate residential facilities. Based on this condition, this research aims to measure the satisfaction level of the residents in Sendangmulyo Housing and the implications for better social housing policy in the future. The results of this research show that the average level of residents’ satisfaction is satisfying, with satisfaction index score of 58.1%. The high satisfaction level is influenced by the quality of community relations with an index of 73.4%, and the low one is influenced by the quality of the environment facilities with an index of 49.8%. The high quality of community relations is caused by the good neighborly relations and social activities that still exist in the neighborhood. It has become a major factor for the social housing residents to stay and survive in the neighborhood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".