Towards a Resilient and Sustainable City: New Paradigm of Flood Disaster Governance Study Case Bekasi City
Bibliographic record
Abstract
Urban faces numerous challenges around the world due to its complexity, rapid urbanization, and diverse urban contexts.Bekasi City facing flood many years as an impact this problem.In early 2020, flooding points in Bekasi City reached 58 points with an area of around 15.37 ha or 73% of the city area with a depth of 1-4 meters.Looking at its history, the longer the flooding, the wider and depth.In 2005 the inundation area ranged from 164 ha, then in 2018, the inundation area increased to 12353 ha.The funds spent on flood management almost onethird of regional budget.The question that should be asked is why flooding still occurs, even though the efforts that have been made are not small.The social, economic and environmental impacts of flooding are significant.If it continues uncontrol, it is feared that the sustainability of Bekasi City is threatened.Therefore, a big picture of the current flood control system in Bekasi City is needed.This study identified that the critical gap in urban environmental risk management strategies in Bekasi City, Indonesia, is the necessity of incorporating community participation and non-structural measures to establish flood-resilient communities and mitigate risk.Addressing this management deficiency will contribute to the realization of a resilient and sustainable Bekasi City.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".