Tipologi Aplikasi Infrastruktur Hijau Skala Komunitas pada Kampung Kota di Indonesia
Bibliographic record
Abstract
This paper addresses one of the knowledge gaps that exists in green infrastructure (GI) discourses, that is the lack of understanding on the application of GI at community scale. GI was first introduced as an alternative to address problems caused by rapid urbanisation. Recently, GI has become a crucial part of strategies to achieve sustainable development and therefore has been widely advocated for its environmental, social, and economic benefits. Although the concepts and benefits of green infrastructure have been widely discussed and recognized in the literature, much of the discussions on GI tend to revolve around its applications at the city and regional scales, while the application of small scale GI at the community level has not been much explored, despite the importance of multiscale principle in the application of GI. To address that issue, this study focuses on the application of small-scale GI at the community level. It employed a systematic review to analyse publications, including articles published in scientific journals and news on reliable mass media on the internet, regarding the application of small-scale GI in 23 Indonesian urban kampung. The study shows that in dense settlements like urban kampung, community scale GI emerges as an alternative solution to the lack of space for GI development. From the 23 cases analysed, GI is mostly intended to function as a mean for environmental conservation and to promote food security.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".