Suburban Development: Spatial and Physical Transformation of Residence as a Determinant of Settlement Densification in Makassar City, Indonesia
Bibliographic record
Abstract
Suburban areas in Indonesia are densifying and transforming in an unsustainable manner, leading to uncontrolled management, spatial utilization, and control.This study aims to analyze transformation as a determinant of densification growth, the effects of physical spatial and residential transformation on densification, and the resulting contribution to settlement densification.The research method involves a combination of quantitative and qualitative approaches with a sequential explanatory design.The results indicate that spatial physical transformation has both a direct and significant impact and an indirect effect on densification through the physical transformation of residences, with an R 2 value of 46.6%.Moreover, physical residence transformation has a direct and significant influence on densification, with an R 2 of 47.8%.The increase in population leads to the spatial and physical transformation of residences, which positively contributes to the process of building density and the level of densification of built settlements.The spatial and physical transformation of residences contribute to changes in typology, morphology, and spatial structure during settlement densification.The morphological change of densification encourages the binary fission of housing units, residential intensification, and spatial agglomeration, as well as the growth of mixed service centers from main roads to neighborhood roads.This research helps formulate development concepts and spatial policy approaches.
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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 | 0.000 | 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 | 0.000 | 0.001 |
| 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".