Strategic Interaction Between the Agglomeration of High-Rise Buildings and the Economy of DKI Jakarta
Bibliographic record
Abstract
Vertical building development has become a strategic solution in accommodating the need for living and working spaces in DKI Jakarta with its land scarcity issues. As a response to the increasing demand for space, the government and experts constantly update the strategic policies for high-rise building development while the developers plan and construct more buildings for business purposes. This study aims to investigate the economic and non-economic factors that affect the development of high-rise buildings and the relationship between the agglomeration of high-rise buildings and the tertiary sector GRDP. To achieve these objectives, we primarily use the data of the number of floors of high-rise buildings collected from Emporis. Other variables are retrieved from the secondary data collected from the official data of the DKI Jakarta government. Pooled OLS estimation of panel data in the 2007-2018 urban village level proves that the strategic interactions occur during the construction of commercial-office and residential high-rise buildings in DKI Jakarta. The estimation result at the city level provides evidence that the presence of commercial-office high-rise buildings in DKI Jakarta generates effective density as it contributes positively to the tertiary sector GRDP. This study provides a new perspective in examining the relationship between the agglomeration of the economy and high-rise buildings in third-world cities, in this case, DKI Jakarta, by using quantitative research through econometric models.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".