TOWARDS GREEN SMART CITY THROUGH PROVIDING OPEN SPACE FOR CITIES IN INDONESIA: SYSTEMATIC AND BIBLIOMETRIC LITERATURE REVIEW
Bibliographic record
Abstract
Smart cities become benchmarks in development cities around the world since the 1990s. In the urban planning context, there are concerns regarding the application of the smart city concept which is considered to only prioritize the progress of cities by technology however no notice to the ecological side of the city. This research discusses the development and arrangement of cities through planning that ensures ecosystem balance, one of which is through open space, which in this case is widely discussed that is green open space. The usual problem that occurs during the development of green open space is not enough land area for available allocation causing an imbalance ecosystem as well as against the sustainability concept. This research method studies literature systematic and bibliometric using Microsoft Excel and VOSviewer from discussion theory and policy about green open space that has been planned in various countries for can applied to cities in Indonesia. The analysis explains the discussion regarding green open space in connection to moving towards a green smart application to cities in Indonesia. Eventually, the findings from this research are discussed related to implementation in the cities in Indonesia which are divided into five aspects, namely political land development, community perception, infrastructure, landscape design, and socio-economic psychology. The final recommendation is that this aspect can be studied further to be applied to cities in Indonesia to realize city smart green future.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.130 | 0.132 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".